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== Lecturers and Seminarists ==

{| class="wikitable" style="text-align:center"
|-
|| Lecturer || [https://www.hse.ru/org/persons/219484540 Samsonov Sergey ] || [svsamsonov@hse.ru] || T902
|- 
|| Seminarist || [https://www.hse.ru/org/persons/225526439 Artur Goldman] || [...] || T926
|-
|}

== About the course ==
This page contains materials for Markov Chains course in 2023/2024 year, mandatory one for 1st year Master students of the MML program (HSE and Skoltech).

Link to telegram chat: https://t.me/+9bDEStkmdi0xMWVi

== Grading == 
The final grade consists of 3 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :
* O<sub>HW</sub> for the hometasks
* O<sub>Mid-term</sub> for the midterm exam
* O<sub>Exam</sub> for the final exam  
The formula for the final grade is 
* O<sub>Final</sub> = 0.35*O<sub>HW</sub> + 0.3*O<sub>Mid-term</sub> + 0.35*O<sub>Exam</sub>
with the usual (arithmetical) rounding rule.

[... '''Table with grades''']

== Lectures ==
* [https://disk.yandex.ru/i/UN0f7EuYK0G9jQ Lecture №1, 09.11]
* [... Lecture №2, 18.11]
* [https://disk.yandex.ru/i/c3wEPZ1zYaCI_g Lecture №3, 25.11]
* [https://disk.yandex.ru/i/CfhWKmbAOaEgEw Lecture №4, 02.12]
* [https://disk.yandex.ru/d/nM_XKLm4BfILhw Lecture №5-6, 16.12]
* [https://disk.yandex.ru/i/I8M-SoWlLHl3Ag Lecture №7, 13.01]
* [https://disk.yandex.ru/i/7F6gH0mFBcnn_w Lecture №8, 20.01]

== Seminars ==
https://disk.yandex.ru/d/cXeyH_vL3fEb_g

== Homeworks ==
To submit homework, join
[https://classroom.google.com/c/NjM4ODgyNjE1ODEw?cjc=y6dk72i '''Google classroom'''] — invite code '''y6dk72i'''

Homework solution file should be readable and well-organized.
* [https://disk.yandex.ru/i/UH2fNs3vp6qtFA Homework 1: Deadline 13.01.2024, 23:59]
* [https://disk.yandex.ru/i/S2MdBKkH4_J0jA Homework 2: Deadline 03.02.2024, 23:59]
* [https://disk.yandex.ru/i/WVysij7PyiKD4Q Homework 3: Deadline 17.03.2024, 23:59]
* [https://disk.yandex.ru/i/xFgnBzy6GNy2Uw Homework 4: Deadline 23.03.2024, 23:59]

== Exam ==
TBD

==Midterm ==
TBD

== Recommended literature (1st term) ==
*http://www.statslab.cam.ac.uk/~james/Markov/ - Cambridge lecture notes on discrete-time Markov Chains
*https://link.springer.com/book/10.1007%2F978-3-319-97704-1 - book by E. Moulines et al, you are mostly interested in chapters 1,2,7 and 9 (book is accessible for download through HSE network)
*https://link.springer.com/book/10.1007%2F978-3-319-62226-2 - Stochastic Calculus by P. Baldi, good overview of conditional probabilities and expectations (part 4, also accessible through HSE network)
*https://elearning.unimib.it/pluginfile.php/583708/mod_resource/content/1/1-conditional-law.pdf - Probability kernels and (regular) conditional probabilities, to the third lecture.

== Useful links ==
Energy based models:
*Tutorial from seminar: https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial8/Deep_Energy_Models.html
*Important work on EBM: https://openai.com/research/energy-based-models
*Other EBM related works of the same author: https://energy-based-model.github.io/Energy-based-Model-MIT/
*Another tutorial on EBM from CVPR 2021: https://energy-based-models.github.io/
*Another tutorial on EBM from Yann LeCun: https://www.cs.toronto.edu/~vnair/ciar/lecun1.pdf
*Comparisson between generative models: https://arxiv.org/pdf/2103.04922.pdf