Data Science - free course from the School of Data Analysis, training 4 semesters, date of December 2, 2023.

Each student must successfully complete at least three courses during the semester. For example, if there are two of them in the main program, then you need to choose one of the special courses.

Knowledge is tested primarily through homework - exams and tests are conducted only in some subjects.

First semester

Mandatory
Algorithms and data structures, part 1

01.Complexity and computational models. Analysis of accounting values ​​(beginning)
02.Analysis of accounting values ​​(end)
03.Merge-Sort and Quick-Sort algorithms

04. Ordinal statistics. Heaps (beginning)
05. Heaps (end)
06.Hashing
07.Search trees (beginning)
08.Search trees (continued)
09.Search trees (end). System of disjoint sets
10. RMQ and LCA tasks
11.Data structures for geometric search
12.The problem of dynamic connectivity in an undirected graph

Python language
01.Language Basics (Part 1)
02.Language Basics (Part 2)
03.Object-oriented programming
04.Error handling
05. Code design and testing
06.Working with strings
07.Memory model
08Functional programming
09.Library review (part 1)
10.Library review (part 2)
11.Parallel computing in Python
12.Advanced work with objects

Machine learning, part 1
01.Basic concepts and examples of applied problems
02.Metric classification methods
03.Logical classification methods and decision trees
04.Gradient linear classification methods
05.Support Vector Machine
06.Multivariate linear regression
07.Nonlinear and nonparametric regression, non-standard loss functions
08.Time series forecasting
09.Bayesian classification methods
10.Logistic regression
11.Search for association rules

Second term

Mandatory
Fundamentals of Statistics in Machine Learning

01.Introduction
02.Main tasks and methods of the theory of statistical inference
03. Distribution estimation and statistical functionals
04.Monte Carlo simulation, bootstrap
05.Parametric estimation
06. Hypothesis testing
07. Reducing the dimensionality of multidimensional data
08.Assessing the sensitivity of the model
09.Linear and logistic regression
10.Methods of design of experiments
11.Various types of regularization in linear regression
12. Nonlinear methods for constructing regression dependencies
13.Nonparametric estimation
14.Bayesian approach to estimation
15.Bayesian approach to regression
16. Bayesian approach to regression and optimization
17.Use of the random Gaussian field model in data analysis problems
18.Use of statistical models and methods in surrogate modeling and optimization problems

Machine learning, part 2
01.Neural network methods of classification and regression
02.Compositional methods of classification and regression
03.Criteria for selecting models and methods for selecting features
04.Ranking
05.Reinforcement learning
06.Learning without a teacher
07.Problems with partial training
08.Collaborative filtering
09. Topic modeling

Third semester

To choose from
Automatic text processing

01Course material
or

Computer vision
The course is devoted to methods and algorithms of computer vision, i.e. extract information from images and videos. Let's look at the basics of image processing, image classification, image search by content, face recognition, image segmentation. Then we’ll talk about video processing and analysis algorithms. The last part of the course is devoted to 3D reconstruction. For most problems we will discuss existing neural network models. In the course we try to pay attention only to the most modern methods that are currently used in solving practical and research problems. The course is largely practical rather than theoretical. Therefore, all lectures are equipped with laboratory and homework, which allow you to try most of the methods discussed in practice. The work is performed in Python using various libraries.

01.Digital image and tonal correction.
02.Basics of image processing.
03.Combining images.
04. Classification of images and search for similar ones.
05. Convolutional neural networks for classification and search for similar images.
06.Object detection.
07. Semantic segmentation.
08.Style transfer and image synthesis.
09.Video recognition.
10.Sparse 3D reconstruction.
11.Dense three-dimensional reconstruction.
12.Reconstruction from one frame and point clouds, parametric models.

Fourth semester

Recommended special courses
Deep learning
01.Course material

Reinforcement learning
01.Course material

Self Driving Cars
The course covers the core components of self-driving technology: localization, perception, prediction, behavioral level, and motion planning. For each component, the main approaches will be described. Additionally, students will become familiar with current market conditions and technological challenges.

01.Overview of the main components and sensors of an unmanned vehicle. Levels of autonomy. Drive by Wire. Self-driving cars as a business product. Ways to evaluate progress in creating drones. Localization basics: gnss, wheel odometry, Bayesian filters.
02.Methods of lidar localization: ICP, NDT, LOAM. Introduction to visual SLAM using ORB-SLAM as an example. Statement of the GraphSLAM problem. Reducing the GraphSLAM problem to a nonlinear least squares method. Selecting the correct parameterization. Systems with a special structure in GraphSLAM. Architectural approach: frontend and backend.
03. Recognition task in a self-driving car. Static and dynamic obstacles. Sensors for the recognition system. Representation of static obstacles. Detection of static obstacles using lidar (VSCAN, neural network methods). Using lidar in conjunction with images to detect statics (semantic image segmentation, depth completion). Stereo camera and getting depth from a picture. Stixel World.
04.Imagining dynamic obstacles in a self-driving car. Neural network methods for detecting objects in 2D. Detection based on Bird-eye view of lidar cloud representation. Using lidar with imagery to detect dynamic obstacles. Car detection in 3D based on pictures (3D boxes fitting, CAD models). Radar-based dynamic obstacle detection. Object tracking.
05.Car driving patterns: rear wheel, front wheel. Path planning. The concept of configuration space. Graph methods for constructing trajectories. Trajectories that minimize jerk. Optimization methods for constructing trajectories.
06.Speed ​​planning in a dynamic environment. ST planning. Predicting the behavior of other road users.