Math 173A Rayan Saab

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MATH 173A Fall 2019
Optimization Methods for Data Science I

Instructor

Rayan Saab
rsaab(-at-)math.ucsd.edu
Office: AP&M 5157
Office Hours:
Wednesday 5:00 pm - 6:00 pm (or by appointment)

Teaching Assistants

Jonathan Pham
j2pham(-at-)ucsd.edu
Office: APM 6414
Office Hours: Friday 4:00-6:00 pm

Ziyan Zhu
ziz276(-at-)ucsd.edu
Office: APM 1121
Office Hours: Thursday ~~1:00-3:00 pm~~ 2:00 - 4:00 pm

Zi Yang
ziy109(-at-)ucsd.edu
Office: APM 1210
Office Hours: Tuesday 2:00-4:00 pm and Wednesday 1:00-3:00 pm

Announcements

Sept. 25 Welcome to Math 173A!
Sept. 27 My lecture notes: Lecture notes 1. (May contain errors or typos, use at your own risk)
Oct. 5 The Gradescope code for this course is 9NRYRR
Oct. 8 My lecture notes: Lecture notes 2. (May contain errors or typos, use at your own risk)
Oct. 13 By popular demand, you may use Python or R, in addition to Matlab, for the programming part of your HW.
Oct. 15 My lecture notes: Lecture notes 3. (May contain errors or typos, use at your own risk)
Oct. 19 HW 2 solutions. (May contain errors or typos, use at your own risk)
Nov. 2 My lecture notes: Lecture notes 4. (May contain errors or typos, use at your own risk)
Nov. 11 My lecture notes: Lecture notes 4 (updated). (May contain errors or typos, use at your own risk)
Nov. 11 My lecture notes: Lecture notes 5. (May contain errors or typos, use at your own risk)
Nov. 13 HW3 Solutions: Homework 3 solutions. (May contain errors or typos, use at your own risk)
Nov. 14 HW2 Solutions: Homework 2 solutions. (May contain errors or typos, use at your own risk)
Nov. 14 HW1 Solutions: Homework 1 solutions. (May contain errors or typos, use at your own risk)
Nov. 14 HW4 Solutions: Homework 4 solutions. (May contain errors or typos, use at your own risk)
Nov. 14 Midterm 1 Solutions: MT 1 solutions. (May contain errors or typos, use at your own risk)
Nov. 14 Extra notes on steepest descent: here . (May contain errors or typos, use at your own risk)
Nov. 14 Midterm 2 Solutions: MT 2 solutions. (May contain errors or typos, use at your own risk)
Nov. 27 My lecture notes: Lecture notes 6. (May contain errors or typos, use at your own risk)
Dec. 4 -- *updated* December 8. My lecture notes: Lecture notes 7. (May contain errors or typos, use at your own risk)
Dec. 7 HW5 Solutions: Homework 5 solutions. (May contain errors or typos, use at your own risk)
Dec. 10 HW6 Solutions: Homework 6 solutions. (May contain errors or typos, use at your own risk)
Dec. 10 Final Exam *Seating Chart*: Here .

Extra problems (mostly from discussion sessions, prepared by your TAs). Beware of typos.

Extra Problems 1(A). (May contain errors or typos, use at your own risk)
Extra Problems 1(B). (May contain errors or typos, use at your own risk)
Extra Problems 2(A). (May contain errors or typos, use at your own risk)
Extra Problems 3(A). (May contain errors or typos, use at your own risk)
Extra Problems 4. (May contain errors or typos, use at your own risk)
Extra Problems 5-6(B). (May contain errors or typos, use at your own risk)
Extra Problems 7(B). (May contain errors or typos, use at your own risk)

Course Resources

Catalog Description.
Introduction to convexity: convex sets, convex functions; geometry of hyperplanes; support functions for convex sets; hyperplanes and support vector machines. Linear and quadratic programming: optimality conditions; duality; primal and dual forms of linear support vector machines; active-set methods; interior methods. Prerequisites: MATH 20C or MATH 31BH and MATH 20F or 31AH. Students who have not completed listed prerequisites may enroll with consent of instructor.

Additional Resources.
While there is no required textbook for the course, (parts of) the following books/resources may be useful:
- For a good resource on SVM's and their primal/dual formulation, you may also refer to these lecture notes from A. Ng.
- For Constrained Optimization, Lagrange multipliers, and duality you may also refer to these lecture notes from Y. Singer.
- For Newton's Method, you may also refer to these lecture notes from from R. Freund.
- Boyd and Vandenberghe, Convex Optimization. Link
- Charles L. Byrne, A First Course in Optimization. Link
- Chong and Zak, Introduction to Optimization, Wiley, 2013
- Pedregal, Introduction to Optimization, Springer, 2006

Matlab:
- Your best friend is the "help" command in MATLAB.
- An introduction to MATLAB is available here.
- You can get Matlab from here.

MATH 173A Fall 2019 Optimization Methods for Data Science I