1. Introduction to the course
Lecture 1 CMS 165
Introduction to the course

2. Logistics Course Details Lectures on Tu/Th at 1pm-2:30pm in Moore B270
Recitations on Wed at 5pm-6pm in Anb 106
Office hours on Tu/Th at 5pm-6pm in Anb 106
We will be using Piazza for discussion forums and announcements 
We will be using Moodle for managing homeworks and grades 
Enrollment key is anandkumar
The course website
TAs:
Ehsan Abbasi
Gautam Goel
Sahin Lale

3. Grading 7 Assignments (35% of final grade)
Project (55% of final grade)
Project Grade Decomposition
Proposal Report: 20%
Proposal Presentation: 10%
Final Report: 50%
Final Presentation: 20%
Participation in class and on Piazza (10% of final grade)

4. Late Assignment Policy
Assignments will be due at 4pm on Friday via Moodle. Students are allowed to use up to 48 late hours. Late hours must be used in units of hours. Specify the number of hours used when turning in the assignment. Late hours cannot be used on the projects. There will be no TA support over the weekends.

5. Grading Policy
Peer grading will be used in this class. You will be assigned to grade three different assignments your classmates. 20% of your assignment grade will be based on the accuracy of your grading your peers assignment.
In order to preserve anonymity in assignment grading please include a cover page on your submission which your name is clear. Do not write your name in any of the pages besides your cover page.
Project Guidelines can be found on course website and Piazza.

6. Collaboration Policy
Homeworks: (taken from CS 1) It is common for students to discuss ideas for the homework assignments. When you are helping another student with their homework, you are acting as an unofficial teaching assistant, and thus must behave like one. Do not just answer the question or dictate the code to others. If you just give them your solution or code, you are violating the Honor Code. As a way of clarifying how you can help and/or discuss ideas with other students (especially when it comes to coding and proofs), we want you to obey the "50 foot rule". This rule states that your own solution should be at least 50 feet away . If you are helping another students but cannot without consulting your solution, don't help them, and refer them instead to a teaching assistant.
Projects: Students are allowed to collaborate fully within their project teams, but no collaboration is allowed between teams.

7. What is the course about?
Cannot do both breadth and depth.
Striking a balance here.
We will explore some proof ideas in lectures, but not whole proofs.
In depth reading on your own.
Main intent: to help you extract useful signal in this environment of large number of AI/ML publications.

8. Course Outline Lecture 1: Introduction. Lecture 11: Robustness in ML
Lecture 2: Optimization: Non-convex analysis.
Lecture 3: Optimization: Competitive problems.
Lecture 4: Spectral Methods: Matrix methods.
Lecture 5: Spectral Methods: Tensor methods.
Lecture 6: Midterm: Presentation of project proposals
Lecture 7: Midterm: Presentation of project proposals
Lecture 8: Representation theory: Neural Networks
Lecture 9: Generalization theory: VC and Radamacher bounds
Lecture 10: Generalization theory: Mystery in deep networks?
Lecture 11: Robustness in ML
Lecture 12: Generative models: likelihood-based
Lecture 13: Generative models: GANs
Lecture 14: Active learning
Lecture 15: Domain Adaptation
Lecture 16: Lifelong learning
Lecture 17: TBD
Lecture 18: TBD
Lecture 19: Final presentation of projects
Lecture 20: Final presentation of projects

9. Probability and Measure
Measure theory: generalization of probability.
Learn about probability spaces and measureability.
Why is it important to see probability through lens of measure theory?
Gives a strong foundation.
You will reduce making mistakes about probability events.

10. Statistics 101 Notion of model. Parametric vs Non-parametric.
Frequentist vs Bayesian.
Likelihood.
Statistics.
Sufficient statistics.
Maximum Likelihood estimator
Bayes estimator

11. ML 101 Supervised vs Unsupervised
Loss functions for supervised learning
Overfitting vs Under-fitting
Neural networks

12. References
Caution: These are detailed materials. You are not expected to master them to understand future lectures. I gave a high-level understanding of most useful concepts in this lecture. Use them as needed.
My notes from previous courses. Available on Piazza.
Probability and stochastic processes by Bruce Hajek
Concentration bounds: J. Tropp,
Detection and Estimation: V. Poor
Machine learning: K. Murphy
Use of sufficient statistics in DL: A. Achille and S. Soatto