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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/208508862 Ilya Levin ] || [ivlevin@hse.ru] || T926
|-
|}

== About the course ==
This page contains materials for Markov Chains course in 2024/2025 year, mandatory one for 1st year Master students of the MML program (HSE and Skoltech).

Link to telegram chat: https://t.me/+OJQ1vl3EmhQ3ZDRi

== Grading ==
The final grade consists of 4 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :
* OHW for the hometasks
* OMid-term for the midterm exam
* OExam for the final exam
* OBonus for bonus tasks
The formula for the final grade is
* OFinal = 0.35*OHW + 0.3*OMid-term + 0.35*OExam + 0.1*OBonus
with the usual (arithmetical) rounding rule.

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

== Lectures ==
* [https://disk.yandex.ru/d/UUDGXL0hsdaK5w] - Folder with video lectures from current academic year
* [https://disk.yandex.ru/d/6MgXynxl25djvA] - Lectures from 2023-2024 academic year, slightly different topics

== Seminars ==
* [https://disk.yandex.ru/d/cXeyH_vL3fEb_g] - Seminars from 2023-2024 academic year, slightly different topics

== Homeworks ==
To submit homework, join
[https://classroom.google.com/c/NzI5OTg2OTc3NDQ5?cjc=da3g4oh '''Google classroom'''] — invite code '''da3g4oh'''

Homework solution file should be readable and well-organized.
* [https://disk.yandex.ru/i/fDGf21JpveBYcA HW 1, deadline: 03.12.2024, 23:59]
* [https://disk.yandex.ru/i/4ZjmuTWLNr5SAw HW 2: deadline: 02.02.2025, 23:59]
* [https://disk.yandex.ru/i/geJSsyvTeNOtrw HW 3: deadline: 08.03.2025, 23:59]
* [https://disk.yandex.ru/i/YQTPh0ecvXa0pg HW 4: deadline: 21.03.2025, 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

MC 2024 - История изменений

imported>Ivlevin: Migrated current public revision from wiki.cs.hse.ru