Inter-faculty program

PROGRAMMING FOUNDATIONS IN PYTHON

This introductory course is designed for students with no previous programming experience who are eager to explore the field.

Its purpose is to reveal the logic and creative possibilities of programming while introducing the principal areas of information technology, the syntax of Python, and the language’s fundamental constructs.

Lecture broadcasts are open to everyone. Enrolled students may complete automatically assessed assignments and receive feedback from instructors throughout the semester.

The course is part of MSU’s broader sequence in artificial intelligence. It provides the tools required for further study, with subsequent offerings covering data analysis, machine learning, neural networks, and scientific computing.

PYTHON PROGRAMMING & DATA ANALYSIS

This course introduces the foundations of programming in Python. Students become familiar with the language’s core structures and programming paradigms while gaining experience with tools for data analysis and visualization.

Its central objective is to establish a practical foundation for working with data through Python.

TOPICS INCLUDE

• Python syntax;
• Core Python collections;
• Data visualization;
• NumPy and Pandas;
• Data extraction and acquisition;
• Programming paradigms.

Lecture broadcasts are open to the wider university community. Enrolled students complete automatically assessed assignments and receive instructor feedback throughout the semester.

The course forms part of artificial intelligence sequence and is recommended alongside the online Mathematics for Data Analysis course. It prepares students to continue into machine learning, neural networks, and scientific computing.

The curriculum introduces the essential tools required for more advanced study in artificial intelligence and data science.

MATHEMATICS FOR DATA ANALYSIS

Designed as the theoretical counterpart to Programming & Data Analysis with Python, this course provides the mathematical foundations required for advanced work in artificial intelligence and data science at Lomonosov MSU.

The first part focuses on numerical linear algebra; the second introduces the core principles of optimization.

CURRICULUM

1. Scalars, vectors, and matrices.
Fundamental operations, computational complexity, vector and matrix norms, and their properties.

2. Unitary matrices and low-rank approximation.
Matrix rank, singular value decomposition, multivariate function approximation, image compression, and recommender systems

3. Systems of linear equations.
Gaussian elimination, LU decomposition, inverse matrices, and condition numbers.

4. Sparse matrices and graph structures.
Storage formats, graph properties, and representative problems involving flows, cuts, and cliques

5. Iterative methods for large-scale linear systems.
Richardson, Chebyshev, and conjugate-gradient methods, together with the principles underlying their construction.

6. Eigenvalue problems and spectral methods.
Eigendecomposition, QR decomposition and the QR algorithm, the power method, clustering, and graph partitioning.

7. Tensors and tensor decompositions.
Tucker, canonical, and tensor-train decompositions, with applications in data compression and computational acceleration.

MACHINE LEARNING FOR APPLIED CHALLENGES

This course introduces one of today’s most consequential scientific fields: machine learning. Participants explore foundational approaches for addressing applied problems and develop a practical command of Python-based tools.

Open lecture broadcasts are available to the wider community. Formally enrolled students complete automatically assessed assignments and receive instructor feedback throughout the semester.

The course is part of Lomonosov MSU’s broader artificial intelligence sequence, created to give students access to current knowledge across AI and data science.

INTRODUCTION TO DEEP LEARNING

This course examines the foundations of deep learning and the use of neural networks across a range of computational tasks. Participants gain hands-on experience with contemporary models while developing an understanding of the theoretical principles behind neural computation.

Practical work spans natural language, computer vision, question-answering systems, and generative models.

Lecture broadcasts are open to all. Enrolled students complete automatically assessed assignments and receive guidance from instructors throughout the semester.

The course belongs to Lomonosov MSU’s artificial intelligence sequence and is recommended after prior study in Python programming and machine-learning fundamentals. The broader sequence provides an up-to-date foundation in artificial intelligence and data science.