Professor Chandrajit Bajaj

• Lecture Hours -- Mon, Wed- 3:30 - 5:00 pm.  GDC 6.202 or Zoom
• Office hours – Mon, Wed 5:00 p.m. - 6:00 p.m. or by appointment ( Zoom  or POB 2.324)
• Contact: bajaj@oden.utexas.edu

NOTE: . All questions related to class should be posted through Piazza. Here is the link to register for Piazza: You can also join via the Piazza Tab on the canvas course page

Teaching Assistant

Trung Nguyen

• Office hours – Mon/Wed 2:00 p.m. - 3:00 p.m. GDC 1.302 - TA station Desk 2 or Zoom
• Contact: trungnguyen@utexas.edu

Note: Please attempt to make reservations a day before for office hours to avoid conflicts. 

Course Motivation and Synopsis

This fall course is on foundational mathematical, statistical and computational learning theory and application of data learned predictive models. Students shall be exposed to  modern machine learning approaches in optimized decision making and multi-player games, involving stochastic dynamical systems,  and optimal control. These latter topics are foundational to  the training of multiple neural networks (agents) both cooperatively and in adversarial scenarios helping optimize the learning of all the agents.

An initial listing of lecture topics  is given in the syllabus below. This is subject to modification, given the background and speed at which we cover ground.  Homework exercises shall be given almost  bi-weekly.  Assignment solutions that are turned in late shall suffer a  10% per day reduction in credit, and a 100% reduction once solutions are posted. There will be a mid-term exam in class. The content will be similar to the homework exercises. A list of  topics will also be assigned as take home final projects, to train the best of  machine learned  decision making (agents). The projects will involve ML programming, oral presentation, and a written report submitted at the end of the semester.

This project shall  be graded, and be in lieu of a final exam.

The course is aimed at senior undergraduates and junior graduate students. Those in the 5-year master's program students, especially in the CS, CSEM, ECE, STAT and MATH. are welcome. You’ll need algorithms, data structures, numerical methods and programming experience (e.g. Python ) as a CS senior, mathematics and statistics at the level of CS, Math, Stat, ECE, plus linear algebra, computational geometry, plus introductory functional analysis and combinatorial and numerical optimization (CS, ECE, CSEM , Stat and Math. students).  

Course Material.

1. [B1] Chandrajit Bajaj (frequently updated)  A Mathematical Primer for Computational Data Sciences 
   
2. [BHK] Avrim Blum, John Hopcroft and Ravindran Kannan. Foundations of Data Science
3. [BV] Stephen Boyd and Lieven Vandenberghe Convex Optimization
4. [M] Kevin Murphy Machine Learning: A Probabilistic Perspective
5. [MU] Michael Mitzenmacher, Eli Upfal Probability and Computing (Randomized Algorithms and Probabilistic Analysis)
6. [SD] Shai Shalev-Shwartz, Shai Ben-David Understanding Machine Learning, From Theory to Algorithms
7. [SB] Richard Sutton, Andrew Barto Reinforcement Learning
8. [Basar] Tamer Basar  Lecture Notes on Non-Cooperative Game Theory.
9. Extra reference materials .

TENTATIVE  COURSE OUTLINE (in Flux). 

Project FAQ

1. How long should the project report be?

Answer: See directions in the Class Project List. For full points, please address each of the evaluation questions as succinctly as possible. Note the deadline for the report is December 07 midnight. You will get feedback on your presentations, that should also be incorporated in your final report.

Assignments, Exam, Final Project

There will be five take-home bi-weekly assignments, one in-class midterm exam and one take home final project (in lieu of a final exam). The important deadline dates are:

• Midterm: Wednesday, October 10, 3:30pm - 5:00pm, BUR 2.220
  
• Final Project Written Report, Due: December 07, 11:59pm

Assignments

There will be five written take-home HW assignments and one take-home final project report. Please refer to the above schedule for assignments and final project report due time.

Assignment solutions that are turned in late shall suffer a 10% per day reduction in credit, and a 100% reduction once solutions are posted.

Course Requirements and Grading

Grades will be based on these factors:

• In-class attendance and participation (5%)
• HW assignments (60% and with potential to get extra credit) 

5 assignments. Some assignments may have extra questions for extra points you can earn. (They will be specified in the assignment sheet each time.)

• In-class midterm exam (15%) 
• First Presentation & Report (10%)
• Final Presentation & Report (15%)  

Students with Disabilities. Students with disabilities may request appropriate academic accommodations from the Division of Diversity and Community Engagement, Services for Students with Disabilities, 471-6259, http://www.utexas.edu/diversity/ddce/ssd . 

Accommodations for Religious Holidays. By UT Austin policy, you must notify the instructor of your pending absence at least fourteen days prior to the date of observance of a religious holiday. If you must miss a class or an examination in order to observe a religious holiday, you will be given an opportunity to complete the missed work within a reasonable time before or after the absence, provided proper notification is given.

Statement on Scholastic Dishonesty. Anyone who violates the rules for the HW assignments or who cheats in in-class tests or the final exam is in danger of receiving an F for the course. Additional penalties may be levied by the Computer Science department,  CSEM  and the University. See http://www.cs.utexas.edu/academics/conduct/