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: TBA
• Time: 11:45 am - 1:25 pm Monday, Thursday
• Location: Forsyth Building 202
• Piazza: piazza.com/northeastern/spring2020/cs7140
• Office Hours: TBA

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
• 20 % class participation

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

Project Latex Template

• Suggested Topic

References (required):

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

Additional References (optional):

• [4] Graphical Models, Exponential Families and Variational Inference
• [5] The Elements of Statistical Learning
• [6] Graphical Model Course