FPMI Courses
Published courses
Foundations of Probability and Measure Theory
Part I. Foundations of Probability
- 1. Probability space. Classical models. Independence
- 2. Probability distributions. Random variables. Expectation
- 3. Covariance, convergence of random variables, limit theorems
Part II. Measure Theory
- 4. Systems of sets. Borel sigma-algebra
- 5. Set functions on semirings and sigma-algebras. Measure
- 6. Outer measure, CarathΓ©odoryβs extension theorem
- 7. Lebesgue measure, Lebesgue-Stieltjes measure
- 8. Measurable functions and their convergence
- 9. Lebesgue integral: integral with respect to a measure
- 10. Product measures
- 11. Lebesgue spaces \(L^p(\mu)\)
- 12. Signed measures, densities
- 13. Differentiating the Lebesgue integral. Absolutely continuous functions
- 1. Basic concepts of the theory of random processes. Some examples
- 2. Simple random walk
- 3. Generating functions. The Galton-Watson branching process
- 4. Distributions on path space
- 5. Renewal processes. The stationary Poisson process
- 6. Discrete-time Markov chains (DTMC)
- 7. Continuous-time Markov chains (CTMC)
- 8. The Wiener process, Gaussian processes
- 9. Markov processes, stopping times
- 10. Martingales
Curriculum
A reference map of courses by semester for the programs βApplied Mathematics and Informaticsβ (Classic) and βInformatics and Computer Engineering: Natural and Computer Sciencesβ (NCS/CSE). It does not include foreign languages, physical education, history and philosophy courses, departmental courses or research work. Courses already published on the site are shown in bold. The set of courses and their placement may vary by year of admission and by program; the site does not replace the official curriculum.
π’ β full course published Β· π‘ β course passport published Β· βͺ β course not yet represented in the catalog.
Semester I
- π‘ Introduction to Mathematical Analysis
- π‘ Algebra and Geometry
- π‘ Algorithms and Data Structures
- π‘ Introduction to Artificial Intelligence
- π‘ C++ Programming
- π‘ Mathematical Logic and Theory of Algorithms (Classic)
- π‘ Fundamentals of Combinatorics and Number Theory (Classic)
- βͺ Algebra of Logic, Combinatorics, Graph Theory (NCS/CSE)
- βͺ General Physics: Mechanics (NCS/CSE)
Semester II
- π‘ Multivariable Analysis, Integrals and Series
- π‘ Algebra and Geometry
- π‘ Algorithms and Data Structures
- π‘ Python Practicum
- π‘ Programming Technologies
- π‘ Mathematical Logic and Theory of Algorithms (Classic)
- π‘ Fundamentals of Combinatorics and Number Theory (Classic)
- π‘ C++ Programming (Classic)
- βͺ General Physics: Thermodynamics and Molecular Physics (NCS/CSE)
- βͺ Fundamentals of Higher Algebra and Coding Theory (NCS/CSE)
Semester III
- π‘ Differential Equations
- π’ Foundations of Probability and Measure Theory (Classic) / βͺ The Lebesgue Integral and Field Theory (NCS/CSE)
- π‘ Multiple Integrals and Field Theory (Classic)
- π‘ Group Theory (Classic)
- π‘ Discrete Analysis 1 (Classic)
- π‘ Formal Languages and Translation (Classic)
- π‘ Computer Architecture and Operating Systems (Classic)
- π‘ Physical Foundations and Logic of Modeling (Classic)
- π‘ Algorithms and Data Structures (NCS/CSE)
- βͺ Analytical Mechanics (NCS/CSE)
- βͺ General Physics: Electricity and Magnetism (NCS/CSE)
- βͺ Introduction to Mathematical Logic (NCS/CSE)
- βͺ Theory of Algorithms and Models of Computation (NCS/CSE)
- βͺ Programming Technologies 2 (NCS/CSE)
Semester IV
- π‘ Harmonic Analysis
- π‘ Differential Equations
- π’ Probability Theory
- π‘ Theory of Rings and Fields (Classic)
- π‘ Discrete Analysis 2 (Classic)
- π‘ Computational Complexity (Classic)
- π‘ Introduction to Data Analysis (Classic)
- π‘ Databases (Classic)
- π‘ Physical Foundations and Logic of Modeling (Classic)
- βͺ Analytical Mechanics (NCS/CSE)
- βͺ Field Theory (NCS/CSE)
- βͺ General Physics: Optics (NCS/CSE)
- βͺ Theory of Computational Complexity (NCS/CSE)
Semester V
- π’ Functional Analysis
- π’ Complex Analysis
- π‘ Machine Learning 1
- π’ Mathematical Statistics
- π‘ Optimization Methods (Classic)
- π‘ Fundamentals of Cryptography (Classic)
- βͺ Practicum in Mathematical Statistics with Python (Classic)
- π‘ Computational Complexity 2 (Classic)
- π‘ Computational Mathematics (NCS/CSE)
- βͺ Optimization Theory (NCS/CSE)
- βͺ Equations of Mathematical Physics (NCS/CSE)
- βͺ General Physics: Quantum Physics (NCS/CSE)
- βͺ Physics Laboratory Practicum (NCS/CSE)
Semester VI
- π’ Functional Analysis
- π’ Random Processes
- π‘ Computational Mathematics (Classic)
- π‘ Parallel and Distributed Computing (Classic)
- βͺ Computational Methods of Algebra (NCS/CSE)
- βͺ Theory and Implementation of Programming Languages (NCS/CSE)
- βͺ Numerical Optimization Methods (NCS/CSE)
For some other programs (MMTU, AI360), individual courses of the probability block may be scheduled in a different semester than shown here β see the full breakdown in _internal/fpmi-curriculum-report.md.