MC 2024 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Ivlevin (обсуждение | вклад) |
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| (не показаны 2 промежуточные версии этого же участника) | |||
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== Lectures == | == Lectures == | ||
| − | * [https://disk.yandex.ru/ | + | * [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 | |
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| − | * [https://disk.yandex.ru/d/ | + | |
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== Seminars == | == Seminars == | ||
| − | https://disk.yandex.ru/d/cXeyH_vL3fEb_g | + | * [https://disk.yandex.ru/d/cXeyH_vL3fEb_g] - Seminars from 2023-2024 academic year, slightly different topics |
== Homeworks == | == Homeworks == | ||
| − | + | TBD | |
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== Exam == | == Exam == | ||
Текущая версия на 15:33, 11 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
- [1] - Folder with video lectures from current academic year
- [2] - Lectures from 2023-2024 academic year, slightly different topics
Seminars
- [3] - Seminars from 2023-2024 academic year, slightly different topics
Homeworks
TBD
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
