MC 2023 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
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*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://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. | *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 | ||
Версия 12:26, 6 марта 2024
Содержание
Lecturers and Seminarists
| Lecturer | Samsonov Sergey | [svsamsonov@hse.ru] | T902 |
| Seminarist | 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) :
- OHW for the hometasks
- OMid-term for the midterm exam
- OExam for the final exam
The formula for the final grade is
- OFinal = 0.35*OHW + 0.3*OMid-term + 0.35*OExam
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 y6dk72i
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
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
