# Advanced Machine Learning

**Course Description: **Covers advanced topics in machine learning. Focuses on probabilistic graphical models, inference and learning algorithms, and their connections to deep learning. This is a PhD level course with emphasis on mathematical principles as well as practical know-how.

The course syllabus will continuously be updated with methods from state-of-the-art research.

**Course Details: **

- Lecturer: Professor Qi (Rose) Yu (roseyu@northeastern.edu)
- TA: Clara De Paolis (depaoliskaluza.m@husky.neu.edu)
- Time: 11:45 am - 1:25 pm Monday, Thursday
- Location: Forsyth Building 202
- Piazza: piazza.com/northeastern/spring2020/cs7140
- Office Hours: 462 WVH Thu 3:00 pm - 4:00 pm/by appointment

**Prerequisite:**

- CS 6140 Machine Learning
- Familiar with linear algebra, statistics, optimization
- Proficient at programming in Python

**Course Assessment:**

- 40 % homework (10% x 4)
- 40 % project
- 15 % paper discussion
- 5 % lecture scribe

**NOTES: **For students who need computing resources for the class project, we recommend you to look into Google Colab or AWS educate program.

**Course Syllabus**

**Note: the syllabus is tentative and is subject to change.**

**Final Project **

**References (required):**

- [1] Information Theory, Inference, and Learning Algorithms
- [2] Probabilistic Graphical Models: Principles and Techniques
- [3] Deep Learning Book

**Additional References (optional):**