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Department of Intelligent Systems

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Phystech School of Applied Mathematics and Informatics MIPT

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The base department graduates bachelors and masters in the direction 010900 “Applied Mathematics and Physics”. Education at the department is in three specializations “Data Science”, “Design and Organization of Intelligent Systems”, “Information Retrieval and Machine Learning”. The base organization is the Computing Center of the Federal Research Center “Informatics and Control” of the Russian Academy of Sciences.

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The base organization is the Computing Center of the Russian Academy of Sciences, the Federal Research Center “Informatics and Control” of the Russian Academy of Sciences. The founder is an academician of the Russian Academy of Sciences and an outstanding Russian mathematician, an expert in the field of mathematical methods of recognition, forecasting and data analysis Konstantin Vladimirovich Rudakov. The department has been actively developing since 2003 within the scientific school of the academician of the Russian Academy of Sciences Yuri Ivanovich Zhuravlev. A significant contribution to the development of the department was made by the professor of the Russian Academy of Sciences, Doctors of Physical and Mathematical Sciences. K.V. Vorontsov and V.V. Strijov.

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Teachers of the department

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20 researchers: professor of the Russian Academy of Sciences, doctors and candidates of sciences. Teachers are young candidates of sciences, graduates of the department Ph.D. Alexander Aduenko, Ph.D. Oleg Bakhteev, Ph.D. Roman Isachenko, graduate student Andrey Grabovoy. The average age of the teachers is 35 years.

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Areas of educational and scientific activities of the department

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Machine learning; multivariate statistics; geometric methods of deep learning; model selection and neural network architectures; ensembles of models and generative neural networks; functional data analysis and analysis of spatio-temporal data.

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Applied Research

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Text analysis and topic modeling, image and video analysis, multivariate time series analysis, biomedical signal analysis, brain-computer interfaces.

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Principles of Scientific Work

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Openness of ideas, projects, results at all stages of study and research; continuous assessment of the quality of ideas and results; communication with the scientific community, updating according to the latest achievements.

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Main courses

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Machine Learning, read by K.V. Vorontsov, is the most famous and complete course in Russia, covering fundamental and modern topics of machine learning and data analysis.

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My first scientific article: automation of scientific research, read by V.V. Strijov, is a course that provides the basics for performing scientific research - from planning to presenting results. Each student chooses a personal project and receives a personal consultant and expert. For three months, the student prepares a scientific paper with theory and computational experiment. The results are presented at scientific conferences, articles are submitted to peer-reviewed journals.

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Main courses (programs)

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  • Machine Learning
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  • Automation of scientific research
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  • Deep learning
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  • Bayesian model selection
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  • Forecasting methods
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  • Deep generative models
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  • Bayesian multimodeling
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  • Probabilistic topic modeling
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  • Natural language processing
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  • Data analysis in metric spaces
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  • Signal processing
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  • Bioinformatics
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  • Software engineering for data analysis
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  • Recommender systems
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Scholarships

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Collaborative training programs

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In 2019, the department organized a double degree and joint master’s program at the University of Grenoble-Alpes. Master’s students study in joint programs at the Paris Polytechnic School, the Skolkovo Institute of Science and Technology, the KAUST University of Science and Technology. Joint research is underway with laboratories UGA, INRIA, CNRS, Ecole Polytechnique, EPFL, Los-Alamos, CMU.

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Internships

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Since the beginning, the department has been actively cooperating with the base companies of the Forexis Group of Companies: Forexis, Antiplagiat, Antirutina, GoodfoCast, Labor Laboratory, Procompliance and participates in joint projects of Forexis Group with the companies MMFB, InterRAO, Yandex, Sberbank, Russian Railways, Samsung, LG.

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Come and study with us and do research on machine learning!

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+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
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+ + diff --git a/admission/index.html b/admission/index.html new file mode 100644 index 00000000..8f914ef8 --- /dev/null +++ b/admission/index.html @@ -0,0 +1,302 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Admission + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Admission format: interview

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Algorithm of admission, bachelors, 2nd year:

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  1. A student fills in the form,
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  3. solves one of the following tasks,
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  5. makes an opinion about the work performed by the students of the department,
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  7. on the appointed date, makes a three-minute report on the task and on the topic or work of interest,
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  9. is waiting for the decision of the dean’s office on the distribution to the department.
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The interview procedure from the point of view of the department teachers:

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1.Gathering the whole group of those wishing to enter the department,

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  1. Listen to a short report of each participant, +
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    • “solving a test problem”,
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    • “my goals for admission to the department” on the material of student works of the department,
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  2. +
  3. Ask questions, look at the questionnaire,
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  5. Draw up a ranked list of applicants, publish [by the link here] and send it to the dean’s office, close the reception.
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The Master admission procedure:

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  1. A student fills the form,
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  3. sends a request to mlalgorithms at gmail
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  5. parts interview with +
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    • BS thesis,
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    • topics and results of the students scientific works at the department,
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    • mathematics of the BS programme: algebra, analysis, probability (functional analysis and measure theory included)
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  6. +
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Test problems (2022 and earlier)

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Link – the spring of 2023 will be published here

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Check the your basic abilities, spring 2023

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Chapters 1, 2, 3 from the Pen and Paper Exercises in Machine Learning by Michael U. Gutmann

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Tips for solving problems (these are just tips, not directions)

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  • A theoretical solution is more important than a practical one.
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  • Write the theory yourself (do not copy the material from the lectures).
  • +
  • Visualize problem solving (freehand drawing is preferable to none).
  • +
  • Put the results of the experiment on slides (do not show pynb).
  • +
  • When discussing neural networks, keep in mind that for us they are not black boxes, but transparent ones.
  • +
  • Take these, or past, or your tasks - which is interesting to you.
  • +
+ +

Report topics for discussion

+

Link

+ + + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/automation_scientific_research/index.html b/course/automation_scientific_research/index.html new file mode 100644 index 00000000..fbc11705 --- /dev/null +++ b/course/automation_scientific_research/index.html @@ -0,0 +1,376 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Automation of Scientific Research + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Automation of Scientific Research

+
+ + + + + +
+ +

About (My first scientific paper)

+

This course produces student research papers. It gathers research teams in a society. Each team combines a student, a consultant and an expert. The student is a project driver, who wants to plunge into scientific research activities. The consultant, a graduated student, conducts the research and helps the student. The expert, a professor, states the problem and enlightens the road to the goal.

+ + + + +

Syllabus

+
    +
  • Select your project and tell about it.
  • +
  • State your problem.
  • +
  • Plan the experiment.
  • +
  • Visualise the principle.
  • +
  • Write the theory.
  • +
  • Analyse the error.
  • +
  • Construct your paper.
  • +
  • Review a paper.
  • +
  • Select a journal to submit.
  • +
  • Prepare your presentation.
  • +
+ +

Labworks

+

A selected project on applied mathematics or machine learning, conducted by a consultant and professor. The delivery is a paper, a code and a talk.

+ +

Grading

+

Each week carries several tasks. Each task brings one point exactly according to the schedule of the course.

+ +

Prerequisites

+

Algebra and analysis at the third year of undergraduate level.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Vadim Strijov +

+
+ + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/bayesian_model_selection/index.html b/course/bayesian_model_selection/index.html new file mode 100644 index 00000000..95ee4864 --- /dev/null +++ b/course/bayesian_model_selection/index.html @@ -0,0 +1,373 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Bayesian Model Selection + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Bayesian Model Selection

+
+ + + + + +
+ +

About

+

The course is dedicated to the basis of Bayesian Methods in Machine Learning, Bayesian Models’ construction and inference. The notion of optimal bayesian forecast is introduced, and the methods for constructing the one are studied. The methods for handling non-linearities and inhomogeneities in data are discussed. Handling of missing data and optimal model evolution with time are considered from a Bayesian perspective.

+ +

Syllabus

+
    +
  • Introduction: Reminding key definitions from Probability Theory and Statistics.
  • +
  • Multiple Hypotheses testing and prior election.
  • +
  • Naive Bayes, its generalizations and optimal bayesian forecast.
  • +
  • Exponential family of distributions and sufficient statistics.
  • +
  • Bayesian Linear Regression. Evidence.
  • +
  • Bayesian Logistics Regression and Feature selection via Max-evidence principle.
  • +
  • EM-algorithm and Variational EM-algorithm.
  • +
  • Gaussian process and optimal model evolution with time.
  • +
  • Constructing adequate multimodels.
  • +
  • Markov Chain Monte-Carlo (MCMC) methods.
  • +
  • Hamiltonian Markov Chain Monte-Carlo (HMC) methods.
  • +
  • Bayesian Optimization.
  • +
+ +

Labworks

+

4 theoretical tasks (performed individually), 1 practical task and 1 competition (performed in teams), 2 tests. +Written and oral exam (the latter can be substituted by the presentation on a selected topic).

+ +

Grading

+

Each theoretical task gives at most 50 / 100 points, while practical one and competition each give up to 50 points. The score for the TOP1 student in each task is doubled (extra points). Written and oral exams add up to 250 together. +The final mark is computed by scaling the total score by maximal one assuming no extra points gained.

+ +

Prerequisites

+

Probability, Basic Machine Learning, Linear Algebra, Optimization.

+ + + +
+

Lecturers

+
+ +
+ + + + +
+

+ + + + Aleksander Aduenko +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/bayesian_multimodeling/index.html b/course/bayesian_multimodeling/index.html new file mode 100644 index 00000000..b055cbfe --- /dev/null +++ b/course/bayesian_multimodeling/index.html @@ -0,0 +1,358 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Bayesian Multimodeling + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Bayesian Multimodeling

+
+ + + + + +
+ +

About

+

The course is devoted to the problem of optimal model selection in machine learning and data analysis tasks. During the course, we investigate the Bayesian approach to model selection, its theoretical and practical aspects. We consider properties of model selection methods both specific to some notable model families or general properties suitable for the most model families.

+ +

Syllabus

+

The course investigates the properties of model selection for different types of models: from linear to mixtures of experts built on deep learning models. Various approaches to model selection and hyperparameter optimization are discussed, including gradient-based methods, sequential model based optimization methods, methods based on hypernetworks and supernetworks.

+ +

Labworks

+

4 practical homeworks and 2 oral talks (optional) every term. There is also a simple questionnaire after each pair.

+ +

Grading

+

The overall score is a sum of scores for practical tasks, talks and the answers for the questionnaire.

+ +

Prerequisites

+

Statistics, linear algebra, machine learning, deep learning, intro to bayesian inference.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + +
+

+ + + + Oleg Bakhteev +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/bioinformatics/index.html b/course/bioinformatics/index.html new file mode 100644 index 00000000..a96fd53c --- /dev/null +++ b/course/bioinformatics/index.html @@ -0,0 +1,373 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Bioinformatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Bioinformatics

+
+ + + + + +
+ +

About

+

In the course of lectures, unique features of biological data are considered, leading to original statements of recognition and classification problems. It should be noted that for almost all the problems considered in the course, accurate and mathematically sound solutions have not yet been proposed. In this sense, the course represents an extensive field of activity for independent scientific work of students. +A system of recognition tasks is formulated that reflects the structure of biological systems and provides a basis for the construction of problem-oriented theories. The fundamentals of the formalism developed to solve the problems of bioinformatics and other poorly formalized problems in the field of advanced biomedical research and sentiment analysis are considered. This formalism is based on the theory of universal and local constraints within the framework of an algebraic approach to recognition. Attention is paid to biomedical applications of the results of intelligent analysis of biological data.

+ +

Syllabus

+
    +
  • From cell biology to recognition tasks.
  • +
  • iological data, objects and approaches to the formalization of tasks.
  • +
  • Tasks 1D to 1D: comparison of character sequences.
  • +
  • 1Ddna tasks. 1Ddna and 3Ddna tasks. Tasks of 1Drna, 2Drna, 3Drna.
  • +
  • X-ray diffraction analysis and NMR of proteins, 3Db to 3Db and 3Db to 2Db tasks.
  • +
  • Development of a problem-oriented theory on the example of the problem of recognition of a secondary structure.
  • +
  • Tasks 1DB to 1Db.
  • +
  • Tasks 1Db to F and 3D to F and genome annotation task.
  • +
  • Analysis and synthesis of biological networks.
  • +
  • Molecular pharmacology and chemoinformatics.
  • +
  • Biomedical and genetic research.
  • +
  • Text analysis, use of databases.
  • +
  • Bio-logic and algorithms.
  • +
+ +

Labworks

+

Practical tasks will be announced during the lectures. Students can formulate the topics of research tasks themselves. After selecting the task, the work requirements are discussed.

+ +

Grading

+

Before the start of the oral exam (report-presentation), it is necessary to submit a report on the research work (3-5 pages) carried out on the selected task.

+ +

Prerequisites

+

Machine learning methods.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Ivan Torshin +

+
+ + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/computational_geometry/index.html b/course/computational_geometry/index.html new file mode 100644 index 00000000..18f1c809 --- /dev/null +++ b/course/computational_geometry/index.html @@ -0,0 +1,366 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Computational Geometry Algorithms + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Computational Geometry

+
+ + + + + +
+ +

About

+

The course discusses the basic algorithms of computational geometry.

+ +

Syllabus

+
    +
  • Geometric search.
  • +
  • Construction of convex hulls.
  • +
  • Defining intersections of objects.
  • +
  • Voronoi diagrams and Delaunay triangulations.
  • +
  • Proximity of geometric objects.
  • +
  • Median axes and skeletons.
  • +
+ +

Labworks

+

The course assumes the solution of several laboratory works.

+ +

Grading

+

The assessment is based on the results of laboratory work and an oral exam.

+ +

Prerequisites

+

Machine learning, data structures and algorithms.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Leonid Mestetskiy +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/deep_generative_models/index.html b/course/deep_generative_models/index.html new file mode 100644 index 00000000..716c7f73 --- /dev/null +++ b/course/deep_generative_models/index.html @@ -0,0 +1,457 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Deep Generative Models + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Deep Generative Models

+
+ + + + + Course Website
+ + +
+ +

About

+

The course is devoted to modern generative models (mostly in the application to computer vision). Special attention is paid to the properties of various classes of generative models, their interrelationships, theoretical prerequisites and methods of quality assessment. The aim of the course is to introduce the student to widely used advanced methods of deep learning.

+ +

Syllabus

+
    +
  1. Generative models overview and motivation. Problem statement. Divergence minimization framework. Autoregressive modelling.
  2. +
  3. Autoregressive models (WaveNet, PixelCNN). Bayesian Framework. Latent Variable Models (LVM). Variational lower bound (ELBO).
  4. +
  5. EM-algorithm, amortized inference. ELBO gradients, reparametrization trick. Variational Autoencoder (VAE).
  6. +
  7. VAE limitations. Posterior collapse and decoder weakening. Tighter ELBO (IWAE). Normalizing flows prerequisities.
  8. +
  9. Normalizing Flow (NF) intuition and definition.. Forward and reverse KL divergence for NF. Linear flows.
  10. +
  11. Autoregressive flows (gausian AR NF/inverse gaussian AR NF). Coupling layer (RealNVP). NF as VAE model.
  12. +
  13. Discrete data vs continuous model. Model discretization (PixelCNN++). Data dequantization: uniform and variational (Flow++). ELBO surgery and optimal VAE prior. Flow-based VAE prior.
  14. +
  15. Flows-based VAE posterior vs flow-based VAE prior. Likelihood-free learning. GAN optimality theorem.
  16. +
  17. Vanishing gradients and mode collapse, KL vs JS divergences. Adversarial Variational Bayes. Wasserstein distance. Wasserstein GAN (WGAN).
  18. +
  19. WGAN with gradient penality (WGAN-GP). Spectral Normalization GAN (SNGAN). f-divergence minimization. Evaluation of implicit models.
  20. +
  21. GAN evaluation (Inception score, FID, Precision-Recall, truncation trick). Discrete VAE latent representations.
  22. +
  23. Vector quantization, straight-through gradient estimation (VQ-VAE). Gumbel-softmax trick (DALL-E). Neural ODE. Adjoint method.
  24. +
  25. Continuous-in-time NF (FFJORD, Hutchinson’s trace estimator). Kolmogorov-Fokker-Planck equation and Langevin dynamic. SDE basics. Score matching (Sliced score matching, denoising score matching).
  26. +
  27. Noise conditioned score network (NCSN). Gaussian diffusion process. Denoising diffusion probabilistic model (DDPM).
  28. +
+ +

Labworks

+

6 homeworks: theory and practice.

+ +

Grading

+

Each homework gives 13 points + an exam for 26 points. Final score: (number of points / 8) - 2.

+ +

Prerequisites

+

Statistics, machine learning, deep learning, intro to bayesian inference.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Roman Isachenko +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Petr Mokrov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/deep_learning/index.html b/course/deep_learning/index.html new file mode 100644 index 00000000..d61a2fa8 --- /dev/null +++ b/course/deep_learning/index.html @@ -0,0 +1,696 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Deep Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Deep Learning

+
+ + + + + +
+ +

About

+

An overview course on basic neural network models and their application to image, text, and sound processing tasks.

+ +

Syllabus

+
    +
  • Feedforward networks. Matrix-vector differentiation. Automatic differentiation.
  • +
  • Neural network optimization. DropOut. PyTorch.
  • +
  • Initialization of neural networks. Types of normalization, convolutional neural networks, implementation of convolutional networks in PyTorch.
  • +
  • Recurrent neural networks. Machine translation task. Attention mechanism. Transformer model.
  • +
  • Object detection.
  • +
  • Semantic segmentation. Instance/Panoptic segmentation.
  • +
  • Reinforcement learning. Markov’s decision making process. Bellman equations.
  • +
  • Policy iteration, Value iteration algorithms. Q-learning. DQN model.
  • +
  • Algorithm Reinforce, A2C algorithm. Multi-armed bandits.
  • +
  • Generative models. Variational autoencoder model.
  • +
  • Autoregressive models: PixelCNN, PixelRNN. Reparametrization trick.
  • +
  • Generative adversarial networks and their modifications. Image quality metrics.
  • +
+ +

Labworks

+

5 practical tasks and 1 lab work. Each task is evaluated from 10 points. The exam is evaluated from 10 points.

+ +

Grading

+

0.7 * semester points + 0.3 * exam points.

+ +

Prerequisites

+

Machine learning, statistics, optimization methods.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Dmitriy Kropotov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Olga Grebenkova +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Andrey Filatov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Anton Bishuk +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Konstantin Yakovlev +

+
+ + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/forecasting_methods/index.html b/course/forecasting_methods/index.html new file mode 100644 index 00000000..8b5eacc9 --- /dev/null +++ b/course/forecasting_methods/index.html @@ -0,0 +1,535 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Forecasting Methods + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Forecasting Methods

+
+ + + + + +
+ +

About

+

The course joins two parts of the problem statements in Machine Learning. The first part comes from the structure of the measured data. The data come from Physics, Chemistry and Biology and have intrinsic algebraic structure. This structure is part of the theory that stands behind the measurement. The second part comes from errors of the measurement. The stochastic nature errors request the statistical methods of analysis. So this course joins algebra and statistics. It is devoted to the problem of predictive model selection.

+ +

Syllabus

+
    +
  • Autoregressive models.
  • +
  • Correlation and Convergence Analysis.
  • +
  • Tensor decompositions.
  • +
  • Neural differential equations.
  • +
  • Continuous time and streams.
  • +
  • Metric methods and tensors.
  • +
  • Spectral methods.
  • +
  • Spectral graph models.
  • +
  • Geometric data analysis.
  • +
+ +

Labworks

+

Two personal labworks on brain data analysis include the problem statement and the computational experiment. The delivery is a two-page report with the problem and experimental results, a code and a talk.

+ +

Grading

+

Questionnaires during lectures (3), two labworks (2+2), the final exam: topics and problems with discussion (3).

+ +

Prerequisites

+

Algebra, analysis and physics at the graduate level.

+ +

Course page

+

with detailed schedule and materials

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Vadim Strijov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Alina Samokhina +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Denis Tikhonov +

+
+ + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/fundamental_ml_theorems/index.html b/course/fundamental_ml_theorems/index.html new file mode 100644 index 00000000..f2df501b --- /dev/null +++ b/course/fundamental_ml_theorems/index.html @@ -0,0 +1,375 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Fundamental Machine Learning Theorems + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Fundamental Machine Learning Theorems

+
+ + + + + +
+ +

About

+

The goal of the course is to learn how to rigorously formulate machine learning problems and to show the role of the mathematical approach. The skills acquired in this course are the basis for writing Bachelor’s, Master’s and Ph.D works in machine learning and applied mathematics. The course includes discussions of axiomatic systems, theorems and their proofs that are relevant in machine learning. Their influence on practical applications is discussed.

+ +

Syllabus

+
    +
  • Gauss-Markov theorem
  • +
  • Singular Value Decomposition theorem
  • +
  • Principal Component Analysis and Karhunen-Loeve Decomposition
  • +
  • Karush-Kuhn-Tucker theorem and optimization
  • +
  • Kolmogorov and Arnold theorems, Tsybenko universal approximator theorem, deep neural network theorem
  • +
  • The free lunch theorem in machine learning, Wolpert
  • +
  • Metric spaces: RKHS Aronzhine, Mercer’s theorem
  • +
  • Scheme theorem, Holland
  • +
  • Convolution theorem, Parseval’s theorem
  • +
  • PAC-learning, compression implies learning
  • +
  • Representation theorem
  • +
  • Theorems about boosting, bootstrap
  • +
  • Variational approximation
  • +
  • Takens’ embedding theorem
  • +
  • Green, Stokes, deRham theorems in geometric learning
  • +
+ +

Labworks

+

Presentation of the formulations, proofs and methods of application, significance, of theorems.

+ +

Grading

+

The quality of presentations and the quality of questions to the speaker from each student.

+ +

Prerequisites

+

Algebra and analysis of the third year of MIPT.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Vadim Strijov +

+
+ + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/image_processing_recognition/index.html b/course/image_processing_recognition/index.html new file mode 100644 index 00000000..ea45928d --- /dev/null +++ b/course/image_processing_recognition/index.html @@ -0,0 +1,373 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Image Processing and Recognition + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Image Processing and Recognition

+
+ + + + + +
+ +

About

+

The course is based on mathematical methods of pattern recognition used for the analysis and classification of images in computer vision systems. +Image transformations are considered in order to generate feature descriptions. The methods of point, spatial geometric, algebraic and inter-frame image processing are studied. Methods of feature generation based on the decomposition of images by basic functions, statistical analysis of image texture, as well as image shape analysis are considered. +Methods of construction of metrics for image comparison (comparison of spectral decompositions, overlay and alignment of images) are considered. And also questions of application of the studied methods in applied problems of computer vision. The tasks of text recognition in document images, the tasks of biometric identification of an individual by the texture of the iris, by the shape of the palm, fingerprint, face profile are considered.

+ +

Syllabus

+
    +
  • Subject and tasks of digital image processing and recognition.
  • +
  • Spatial image processing methods.
  • +
  • Geometric and algebraic methods of image processing.
  • +
  • Methods of inter-frame image processing.
  • +
  • Image analysis based on decomposition by basic functions.
  • +
  • Statistical methods of texture analysis.
  • +
  • Methods of image shape analysis.
  • +
  • Metrics for measuring image similarity.
  • +
  • Text recognition based on document images.
  • +
  • Biometric identification based on image recognition.
  • +
  • Dynamic scene recognition.
  • +
+ +

Labworks

+

Homeworks on image processing and classification.

+ +

Grading

+

The assessment is based on the results of laboratory work and an oral exam.

+ +

Prerequisites

+

Machine learning, linear algebra.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Leonid Mestetskiy +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/index.html b/course/index.html new file mode 100644 index 00000000..7ded250b --- /dev/null +++ b/course/index.html @@ -0,0 +1,557 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Course + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + diff --git a/course/intellectual_data_analysis/index.html b/course/intellectual_data_analysis/index.html new file mode 100644 index 00000000..39bd223e --- /dev/null +++ b/course/intellectual_data_analysis/index.html @@ -0,0 +1,432 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Intellectual Data Analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Intellectual Data Analysis

+
+ + + + + +
+ +

About

+

The course introduces students to the full cycle of creating machine learning models for research purposes. +The main stages of the semester: analysis of an applied problem and formulation of the problem, building a model, performing a computational experiment, error analysis. +The result of the work of a group of students is a library of models for a fixed applied problem or class of problems.

+ +

Course page

+ +

Grading

+

Evaluated: the student’s qualifications and his personal contribution to the project.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Vadim Strijov +

+
+ + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Oleg Bakhteev +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/introduction_machine_learning/index.html b/course/introduction_machine_learning/index.html new file mode 100644 index 00000000..43603662 --- /dev/null +++ b/course/introduction_machine_learning/index.html @@ -0,0 +1,383 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Introduction to Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Introduction to Machine Learning

+
+ + + + + Course Website
+ + +
+ +

About

+

The course covers the main tasks of teaching by use cases: classification, clustering, regression, dimension reduction. The methods of their solution, both classical and new, created over the past 10-15 years, are being studied. The emphasis is on a deep understanding of the mathematical foundations, relationships, advantages and limitations of the methods under consideration. Theorems are mostly given without proofs.

+ +

Syllabus

+
    +
  • Linear classifier and stochastic gradient.
  • +
  • Metric methods of classification and regression.
  • +
  • Support vector machine.
  • +
  • Linear regression. Nonlinear regression.
  • +
  • Model selection criteria and feature selection methods.
  • +
  • Logical classification methods.
  • +
  • Linear ensembles. Advanced methods of ensembling.
  • +
  • Density estimation and Bayesian classification.
  • +
  • Clustering and partial learning.
  • +
  • Deep neural networks.
  • +
  • Deep neural networks for unsupervised learning.
  • +
  • Vector representations of texts and graphs.
  • +
  • Attention models and transformers.
  • +
  • Topic modeling.
  • +
  • Learning to rank.
  • +
  • Recommendation systems.
  • +
  • Search for associative rules.
  • +
  • Adaptive forecasting methods.
  • +
  • Incremental and online learning.
  • +
  • Reinforcement learning.
  • +
  • Active learning.
  • +
+ +

Labworks

+

No.

+ +

Grading

+

At the end of the course, students take an oral exam on all topics of the course.

+ +

Prerequisites

+

Probability theory, statistics, optimization methods, linear algebra.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Konstantin Vorontsov +

+
+ + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/natural_language_processing/index.html b/course/natural_language_processing/index.html new file mode 100644 index 00000000..5108e4ab --- /dev/null +++ b/course/natural_language_processing/index.html @@ -0,0 +1,445 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Natural Language Processing + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Natural Language Processing

+
+ + + + + +
+ +

About

+

The course covers the main tasks and mathematical methods of natural language processing.

+ +

Syllabus

+
    +
  • Preprocessing, feature extraction and classification.
  • +
  • Vector representations of words.
  • +
  • The task of marking sequences (tagging). The Linear-CRF model.
  • +
  • Recurrent neural network models: GRNN, LSTM.
  • +
  • Machine translation. The Sequence-to-sequence approach. The mechanism of attention.
  • +
  • Transformer architecture.
  • +
  • The task of language modeling.
  • +
  • Statistical and neural network language models.
  • +
  • The task of generating a natural language.
  • +
  • Contextual vector representations of words.
  • +
  • Transfer learning to NLP.
  • +
  • The BERT model and its modifications.
  • +
  • Text classification.
  • +
  • Topic modeling and its applications.
  • +
+ +

Labworks

+

The course includes four practical tasks and an exam. The deadline for completing each task is 2 weeks. You can get up to 10 points for each task. For each day of delay, a penalty of 1 point is assigned.

+ +

Grading

+

0.7 * hw points / 5 + 0.3 * exam points.

+ +

Prerequisites

+

Machine learning, deep learning, Python.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Artem Popov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/networks_text_analysis/index.html b/course/networks_text_analysis/index.html new file mode 100644 index 00000000..28b927e0 --- /dev/null +++ b/course/networks_text_analysis/index.html @@ -0,0 +1,373 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Networks and Text Analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Networks and Text Analysis

+
+ + + + + +
+ +

About

+

The methods and technologies used in data mining and based on the concepts of similarity, proximity, analogy are considered. The idea of similarity is inherent in human thinking, it has given rise to a whole range of approaches for all fundamental tasks of IAD, among which the main focus of the course is on classification, regression recovery, clustering, recovery of missing data. +The theoretical basis for the construction, implementation and analysis of a wide range of models and methods of IAD is presented. Methods of constructing and calculating similarity functions, matching similarities on various sets of objects, synthesis of new ways of comparing objects based on existing ones are considered. A complex of technologies designed for efficient representation and processing of metric information by computing systems is considered. +Heuristic data models describing the initial information about recognition objects based on various implementations of the concept of similarity are investigated. The problems requiring solutions in the implementation of these models are considered. Special data structures and algorithms are being studied to effectively configure and use the studied models.

+ +

Syllabus

+
    +
  • The main approaches to the task of similarity.
  • +
  • Classical definition of metric and metric space.
  • +
  • Local metrics and their extension to the entire space.
  • +
  • Geometric subsets of general metric spaces.
  • +
  • Examples of metric spaces.
  • +
  • Classification of similarity functions.
  • +
  • Characteristics of metrics.
  • +
  • Metric transformations.
  • +
  • Implementation of metrics.
  • +
  • The principle of self-organization.
  • +
  • Metrics on finite sets.
  • +
  • Decomposition of MC by finite systems of MC.
  • +
+ +

Labworks

+

No.

+ +

Grading

+

Exam based on course materials.

+ +

Prerequisites

+

Machine learning, algorithms and data structures.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Archil Meysuradze +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/neural_architecture_search/index.html b/course/neural_architecture_search/index.html new file mode 100644 index 00000000..08990f47 --- /dev/null +++ b/course/neural_architecture_search/index.html @@ -0,0 +1,372 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Neural Architecture Search + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ + + + + +
+ +

About

+

The course is devoted to modern methods of constructing complex neural network architectures and choosing optimal structures.

+ +

Syllabus

+
    +
  • Overview of neural network types and architecture descriptions.
  • +
  • Genetic algorithms from GMDH to WANN.
  • +
  • Quality criteria for selecting structures (to be discussed in a couple of weeks).
  • +
  • A priori assumptions about the model structure, structural parameters distribution.
  • +
  • Methods for optimization of structural parameters.
  • +
  • Online training and multi-armed bandits to architecture search.
  • +
  • Reinforcement learning for architecture search.
  • +
  • Knowledge transfer between neural networks and optimization of structural parameters.
  • +
  • Random processes for architecture search.
  • +
  • Generative adversarial networks and the architecture search.
  • +
  • Generation and rejection of structures.
  • +
  • Two-level Bayesian selection and Metropolis-Hastings sampling.
  • +
+ +

Самостоятельная работа

+

The laboratory work consists in the study of the architecture search method. The first job is to analyze a ready-made method, the second job is to propose and program your own method. The work report is a page of text with a formal description of the method with sufficient detail for code recovery and error analysis (basic criteria complexity, stability, accuracy). The interface to the class is fixed and common to everyone, as are the selections. There is a general table with the results, and a private error analysis of each method.

+ +

Оценивание

+

A total of 10 points, two points for answering questions during lectures, four points each for two laboratory work. It is not the accuracy of the approximation that is evaluated, but the quality of the code and error analysis.

+ +

Требуемые знания

+

Machine learning, deep learning, Bayesian model selection.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Mark Potanin +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/optimization_methods/index.html b/course/optimization_methods/index.html new file mode 100644 index 00000000..3990fe75 --- /dev/null +++ b/course/optimization_methods/index.html @@ -0,0 +1,354 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Optimization methods in Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Optimization methods in Machine Learning

+
+ + + + + +
+ +

About

+ +

Syllabus

+ +

Labworks

+ +

Grading

+ +

Prerequisites

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + +
+

+ + + + Aleksandr Beznosikov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/probabilistic_topic_models/index.html b/course/probabilistic_topic_models/index.html new file mode 100644 index 00000000..a6f1c125 --- /dev/null +++ b/course/probabilistic_topic_models/index.html @@ -0,0 +1,371 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Probabilistic Topic Models + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Probabilistic Topic Models

+
+ + + + + +
+ +

About

+

The special course studies probabilistic topic modeling of collections of text documents. The topic model determines which topics are contained in a large text collection, and which topics each document belongs to. Topic models allow you to search for texts by meaning, not by keywords, and create a new generation of information search engines based on the paradigm of semantic exploratory search (exploratory search). Topic models for classification, categorization, segmentation, summarization of natural language texts, as well as for recommendation systems, analysis of bank transactional data, analysis of biomedical signals are considered. The special course develops a multi-criteria approach to the construction of models with specified properties - additive regularization of topic models (ARTM). It is based on the regularization of incorrectly set stochastic matrix decomposition problems. Particular attention is paid to the methods of linguistic regularization for modeling the coherence of the text. Students are supposed to conduct numerical experiments on model and real data using the BigARTM topic modeling library.

+ +

Syllabus

+
    +
  • The task of topic modeling.
  • +
  • Online EM algorithm and regularizers.
  • +
  • Exploratory information search.
  • +
  • Assessment of the quality of topic models.
  • +
  • BigARTM and basic tools.
  • +
  • EM algorithm theory.
  • +
  • Bayesian training of the LDA model.
  • +
  • Topic models of word compatibility.
  • +
  • Dependency analysis.
  • +
  • Multimodal topic models.
  • +
  • Modeling the local context.
  • +
  • Summarization and visualization.
  • +
+ +

Labworks

+

No.

+ +

Grading

+

The condition for passing the course is the performance of individual practical tasks.

+ +

Prerequisites

+

Linear algebra, mathematical analysis, probability theory.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Konstantin Vorontsov +

+
+ + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/recommender_systems/index.html b/course/recommender_systems/index.html new file mode 100644 index 00000000..3bc06337 --- /dev/null +++ b/course/recommender_systems/index.html @@ -0,0 +1,457 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Recommender Systems + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Recommender Systems

+
+ + + + + Course Website
+ + +
+ +

About

+

Course objective is to provide comprehensive introduction to the field of Recommender Systems.

+ +
    +
  • first part of the course will cover theoratical background for RecSys
  • +
  • next, we will discuss practical aspects of recommender system training and evaluation
  • +
  • finally we will briefly cover counterfactual evaluation from logged feedback
  • +
+ +

Syllabus

+
    +
  1. Introduction
  2. +
  3. Neighborhood-Based models
  4. +
  5. Matrix Factorization models
  6. +
  7. Content-based and Hybrid systems
  8. +
  9. Two-level models
  10. +
  11. Neural recommenders
  12. +
  13. Multi-armed bandits
  14. +
  15. Counterfactual evaluation
  16. +
+ +

Labworks

+

Two homeworks during the course

+ +

Grading

+

Based on homework results

+ +

Prerequisites

+

Machine learning, deep learning

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Alexey Grishanov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Anna Volodkevich +

+
+ + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/rnd_in_ai/index.html b/course/rnd_in_ai/index.html new file mode 100644 index 00000000..219428f4 --- /dev/null +++ b/course/rnd_in_ai/index.html @@ -0,0 +1,401 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Creation of Intelligent Systems + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Creation of Intelligent Systems

+
+ + + + + Course Website
+ + +
+ +

About

+

This is the extension of “Automation of Scientific Research” course. The work is organized in teams (2-3 people). Students will prepare weekly presentation of the team’s work: 5 minutes presentation + 5 minutes discussion with other students.

+ +

Criteria for the project:

+
    +
  1. The entire project must be on GitHub under an OpenSource license (MIT)
  2. +
  3. The project must contain the code and instructions for launching it. +
      +
    1. The basic code in jupyter notebook to demonstrate the concept of work and plotting from the article should be run with colab.
    2. +
    3. The source code of the computational experiment should be run on a Unix system by executing two commands (possibly in a special docker image): +
        +
      • python3 train.py - for training models.
      • +
      • python3 test.py - for testing, obtaining the results of the experiment.
      • +
      +
    4. +
    5. The documentation for the code is sphinx.
    6. +
    +
  4. +
  5. The project should contain the manuscript of the article using a stylistic from arXiv - for unification.
  6. +
  7. If the project implies non-synthetic data, then there should be an instruction for obtaining this data, as well as a script for obtaining them. If the data is specific, then they need to be posted on one of the file storages.
  8. +
+ +

Syllabus

+
    +
  • Introductory lecture.
  • +
  • Projects discussion.
  • +
  • Project structure.
  • +
  • The first checkpoint.
  • +
  • Paper structure.
  • +
  • How to build a jupiter notebook correctly.
  • +
  • The second checkpoint.
  • +
  • The correct code is not in jupiter notebook!
  • +
  • Data storage for an experiment.
  • +
  • The third checkpoint.
  • +
  • Code documentation using sphinx.
  • +
  • Docker for the experiment code.
  • +
  • The fourth checkpoint.
  • +
  • Evaluation.
  • +
+ +

Labworks

+

Writing scientific paper.

+ +

Grading

+

There are 4 checkpoints in the course, 3 points are given for each

+
    +
  1. Research of the subject area - analysis of the problem.
  2. +
  3. Theoretical results.
  4. +
  5. Computational experiment.
  6. +
  7. Github project.
  8. +
+ +

Prerequisites

+

Machine learning, deep learning, Python.

+ + + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Andrey Grabovoy +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/signal_processing/index.html b/course/signal_processing/index.html new file mode 100644 index 00000000..f00b3ba8 --- /dev/null +++ b/course/signal_processing/index.html @@ -0,0 +1,368 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Signal Processing + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Signal Processing

+
+ + + + + +
+ +

About

+

The course is devoted to the use of methods of mathematical modelling and theoretical physics in signal processing. It applies methods for machine learning model selection to various types of signals: image, video and sound. It discusses techniques to construct systems of heterogenous signal models.

+ +

Syllabus

+
    +
  • Multi-view, kernels and metric spaces, RKHS for brain Imaging.
  • +
  • Spherical harmonics for mechanical motion modelling.
  • +
  • Riemannian geometry on Shapes and diffeomorphisms for fMRI.
  • +
  • Manifolds, Levi-Chivita connection and curvature tensors.
  • +
  • Differential forms and fibre bundles with examples.
  • +
  • Navier-Stokes equations and viscous flow.
  • +
  • Geometric algebra, exterior product and quaternions.
  • +
  • Graph convolution and continuous Laplace operators, diffusion models.
  • +
  • Homology and cohomology, differential forms for model ensembling.
  • +
+ +

Labworks

+

Two personal labworks on video signals include the problem statement and the computational experiment. The delivery is a two-page report with the problem and experimental results, a code and a talk.

+ +

Grading

+

Questionnaires during lectures (3), two labworks (2+2), the final exam: topics and problems with discussion (3).

+ +

Prerequisites

+

Algebra, analysis and physics at the postgraduate level.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Vadim Strijov +

+
+ + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/course/software_engineering_data_analysis/index.html b/course/software_engineering_data_analysis/index.html new file mode 100644 index 00000000..8876ceb2 --- /dev/null +++ b/course/software_engineering_data_analysis/index.html @@ -0,0 +1,372 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Software Engineering for Data Analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+

Software Engineering for Data Analysis

+
+ + + + + +
+ +

About

+

The course covers the development of data processing systems and machine learning.

+
    +
  • How to bring the developed model into production?
  • +
  • What should I do to work together on high-tech software?
  • +
  • How not to lose previously achieved research results?
  • +
+ +

As a result of mastering the course, students acquire the skills of industrial development of machine learning algorithms, conducting repeatable experiments, using big data, testing and project execution processes in the field of machine learning.

+ +

Syllabus

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    +
  • Structuring data processing programs (CODE).
  • +
  • Writing reliable code (CODE).
  • +
  • System design and data storage in machine learning (DATA) systems.
  • +
  • Processes and types of work in the Data Analysis Project (PROC).
  • +
  • Ensuring repeatability of results (PROC).
  • +
  • Testing of high-tech software (TEST).
  • +
+ +

Labworks

+

Practical tasks during the course + course project.

+ +

Grading

+

The final grade for the course is formed from the success of completing tasks during the semester - 10% for each task, and confirmation on an oral survey of ownership of each of the topics of the course (CODE, TEST, DATA, PROC) - 40% of the grade.

+ +

Prerequisites

+

Python.

+ + + +
+

Lecturers

+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

+ + + + Anton Khritankov +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
+ + diff --git a/docker_test.sh b/docker_test.sh new file mode 100644 index 00000000..3ee93dbd --- /dev/null +++ b/docker_test.sh @@ -0,0 +1,3 @@ +# this is an extremely ineffective one-liner for testing our site +docker run --rm -it -v $(pwd):/data -p 4000:4000 -e LANG=C.UTF-8 -it ubuntu:20.04 /bin/bash -c "cd /data; apt-get update; apt-get install -y bundler; bundle install; bundle exec jekyll serve --host 0.0.0.0" + diff --git a/education/index.html b/education/index.html new file mode 100644 index 00000000..f2ac2852 --- /dev/null +++ b/education/index.html @@ -0,0 +1,582 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Educational program schedule + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + +

Расписание

+ +

Осенний семестр, 2024

+ +

Занятия онлайн

+ + +

Расписание занятий

+ +

4 курс, 7 семестр - вторник

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ВремяПредметПреподавателиФорма отчетностиЗачетные единицы
10:30-12:00Методы глубокого обучения: лекцияФилатов А.В., Яковлев К.Д.Дифф. зачет2
12:10-13:40Методы глубокого обучения: семинарФилатов А.В., Яковлев К.Д.  
14:30-16:00Байесовский выбор моделейАдуенко А.А., Яковлев К.Д.Дифф. зачет1
16:10-17:40Математические методы прогнозированияСамохина А.М., Тихонов Д.М.Дифф. зачет1
+ +

5 курс, 9 семестр - вторник

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ВремяПредметПреподавателиФорма отчетностиЗачетные единицы
14:30-16:00Создание интеллектуальных системГрабовой А.В.Дифф. зачет2
16:10-17:40Байесовское мультимоделированиеБахтеев О.Ю.Дифф. зачет2
18:00-19:30Порождающие модели машинного обучения: лекцияИсаченко Р.В., Кондратенко В.Экзамен3
19:30-21:00Порождающие модели машинного обучения: семинарИсаченко Р.В., Кондратенко В.  
+ +

6 курс, 11 семестр - четверг

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ВремяПредметПреподавателиФорма отчетностиЗачетные единицы
10:30-12:00Обработка сигналовСеверилов П.А.Экзамен3
12:10-13:40Интеллектуальный анализ данныхСтрижов В.В., Бахтеев О.Ю.Зачет2
14:30-16:00Вероятностные тематические моделиВоронцов К.В.Дифф. зачет2
+ +

Ключевые даты

+
    +
  • 2 сентября - завершение приема заявок на научную стипендию
  • +
  • 6 сентября - начало занятий на кафедре
  • +
  • 11,12 сентября - научная ориентация и обсуждение требований к НИР
  • +
  • 5 октября - презентация кафедры для 3 курса (вечером, офлайн)
  • +
  • 2 ноября - собеседование на кафедру (план, онайн)
  • +
  • 12 ноября - окончание приема тезисов на конференцию ММРО
  • +
  • 15-21 декабря - зачетная неделя для 3–6 курсов
  • +
  • 2-15 декабря - конференция ММРО
  • +
  • 21 декабря - зачёт по НИР для 3–6 курсов и аспирантов
  • +
  • 18 января - гос. экзамен 6 курса
  • +
+ +

Весенний семестр, 2025

+ +

3 курс, 6 семестр – четверг

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ВремяПредметПреподавателиФорма отчетностиЗачетные единицы
12:10-13:40Введение в машинное обучениеГрабовой А.В., Воронцов К.В.Дифф. зачет1
14:30-16:00Практикум по программированию на языке ПитонАпишев М.А.Зачет1
16:10-17:40Создание интеллектуальных системГрабовой А.В.Зачет1
17:50-19:20Автоматизация научных исследований (Моя первая научная статья)Грабовой А.В., Бахтеев О.Ю.Дифф. зачет1
+ +

4 курс, 8 семестр – вторник

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ВремяПредметПреподавателиФорма отчетностиЗачетные единицы
10:30-12:00Рекомендательные системыГришанов А.В., Володкевич А.А.Дифф. зачет2
12:50-14:20Методы оптимизации в машинном обученииБезносиков А.Н.Дифф. зачет1
14:30-16:00Байесовский выбор моделейАдуенко А.А.Экзамен2
16:10-17:40Математические методы прогнозированияСамохина А.М.Дифф. зачет3
+ +

5 курс, 10 семестр – вторник

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ВремяПредметПреподавателиФорма отчетностиЗачетные единицы
10:30-12:00БиоинформатикаТоршин И.Ю.Дифф. зачет1
12:50-14:20Методы оптимизации в машинном обученииБезносиков А.Н.Дифф. зачет2
14:30-16:00Байесовское мультимоделированиеБахтеев О.Ю.Экзамен2
16:10-17:40Планирование проектов по созданию программного обеспеченияХританков А.С.Дифф. зачет1
17:50-19:20Создание интеллектуальных системГрабовой А.А.Экзамен2
+ +

Ключевые даты

+

+ +

+
    +
  • 6 февраля - начало занятий
  • +
  • 9 февраля - завершение приема заявок на научную стипендию
  • +
  • 4 марта - поднедельник 18:30 рассказ о кафедре для студентов второго курса, офлайн
  • +
  • 5 марта - завершение приема работ на конференцию МФТИ
  • +
  • апрель - конференция и обсуждение ВКР 4 и 6 курсов
  • +
  • 16-22 мая зачетная неделя для 3–6 курсов
  • +
  • 18 мая - зачёт по НИР для 3–6 курсов и аспирантов
  • +
  • 1 июня - кандидатский экзамен у аспирантов
  • +
  • 8 июня - предзащита 6 курса
  • +
  • 15 июня - ГИА у выпускающихся аспирантов
  • +
  • 15 июня - предзащита 4 курса
  • +
  • 20 июня - в 13:00 защита 6 курса, очно
  • +
  • 22 июня - научный доклад у выпускающихся аспирантов
  • +
  • 27 июня - в 13:00 защита 4 курса, очно
  • +
+ +

Документы и ссылки

+
    +
  • График учебного процесса МФТИ: ссылка
  • +
  • Шаблон отчета по НИР: ссылка.
  • +
  • Программа государственного экзамена для 6 курса: ссылка.
  • +
+ + + +
+ +
+ Department of Intelligent Systems
+ Earth
+ + © 2003- + Intelligent Systems. All rights reserved. +
+
+ + +
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