Advanced Machine Learning
Org Info and Grading
Course annotation
A comprehensive and cutting-edge course designed for students and practitioners
aiming to deepen their understanding of the latest advancements in the field.
This course explores a variety of advanced topics, including unsupervised learning,
the Expectation-Maximization (EM) algorithm, and the exciting world of
Large Language Models (LLMs).
Additionally, the course delves into the realms of Reinforcement Learning,
and addresses the potential of
Generative Adversarial Networks (GANs) and Diffusion models.
Practical coursework, including programming assignments
and hands-on projects, will be primarily based on Python and PyTorch.
Schedule
On Tuesday, 9:00 — 12:00. Course starts on February 4
Course Page
https://avalur.github.io/advanced_ml.html
Prerequisites
We invite 2+ year students with knowledge of higher mathematics and the basics of machine learning.
Assignments
| No. | Topic | Type | Start | Finish | Points | Est. Time |
|---|---|---|---|---|---|---|
| 1 | Unsupervised Learning | practice | Feb 11 | Feb 23 | 100 | 10 h |
| 2 | AlphaZero From Scratch | practice | Feb 25 | Mar 16 | 100 | 20 h |
Midterm
| No. | Topic | Type | Start | Finish | Points | Est. Time |
|---|---|---|---|---|---|---|
| 3 | Agents 101 | practice | Mar 18 | Mar 22 | 100 | 10 h |
| 4 | EM and Neural Networks | theory | Mar 23 | Apr 14 | 100 | 10 h |
| 5 | Build your Own Jarvis | practice | Apr 15 | May 12 | 100 | 40 h |
Course Work
Exam
The final exam will take the form of the blogpost on the internet.
Formula of the final score is the following:
FS = (Midterm)*20% + (Course work)*30% + (Blogpost)*50%