MC 2024 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
(Новая страница: «== Lecturers and Seminarists == {| class="wikitable" style="text-align:center" |- || Lecturer || [https://www.hse.ru/org/persons/219484540 Samsonov Sergey ] || […») |
Ivlevin (обсуждение | вклад) |
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| Строка 40: | Строка 40: | ||
== Homeworks == | == Homeworks == | ||
To submit homework, join | To submit homework, join | ||
| − | [https://classroom.google.com/c/ | + | [https://classroom.google.com/c/NzI5OTg2OTc3NDQ5?cjc=da3g4oh '''Google classroom'''] — invite code '''da3g4oh''' |
Homework solution file should be readable and well-organized. | Homework solution file should be readable and well-organized. | ||
Версия 18:10, 8 ноября 2024
Содержание
Lecturers and Seminarists
| Lecturer | Samsonov Sergey | [svsamsonov@hse.ru] | T902 |
| Seminarist | Ilya Lewin | [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
- Lecture №1, 09.11
- [... Lecture №2, 18.11]
- Lecture №3, 25.11
- Lecture №4, 02.12
- Lecture №5-6, 16.12
- Lecture №7, 13.01
- Lecture №8, 20.01
Seminars
https://disk.yandex.ru/d/cXeyH_vL3fEb_g
Homeworks
To submit homework, join Google classroom — invite code da3g4oh
Homework solution file should be readable and well-organized.
- Homework 1: Deadline 13.01.2024, 23:59
- Homework 2: Deadline 03.02.2024, 23:59
- Homework 3: Deadline 17.03.2024, 23:59
- 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
