Week 1 (9/2) - Course Logistics
Week 2 (9/9) - Introduction and Set Theory
Week 3 (9/16) - Measure Theory and Probability Theory
Week 4 (9/23) - Random Variable
Week 5 (9/30) - Random process and Gaussian Process
Week 6 (10/7) - Functional Analysis
Week 7 (10/14) - Interim presentation 1
Week 8 (10/21) - Interim presentation 2
Week 9 (10/28) - Interim presentation 3
Week 10 (11/4) - Bayesian Deep Learning
Week 11 (11/11) - Uncertainty in Deep Learning
Week 12 (11/18) - Active Learning
Week 13 (11/25) - Robust Learning
Week 14 (12/2) - Final presentation 1
Week 15 (12/9) - Final presentation 2
Week 16 (12/16) - Final presentation 3
Homework: 10%
Participation: 20%
Midterm: 30%
Final: 40%
This syllabus is subject to further change at the instructor's discretion.