Logistics

• Textbooks
• Grading
• Participation
• Scribe Duties
  • Regrade Policy
• Final Project

Textbooks

The required textbook for this class is (note that the material of the class goes beyond this book):

• Boyd & Vandenberghe. Convex Optimization. Cambridge University Press. 2003
• Bishop. Pattern Recognition and Machine Learning. Springer. 2006
• Wainwright & Jordan. Graphical Models, Exponential Families, and Variational Inference. Now Publishers, Inc. 2008
• Mohri, Rostamizadeh, & Talwalkar. Foundations of Machine Learning. MIT Press. 2018
• Putman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc. 1994

Grading

The grading breakdown is as follows:

• Participation (20%)
• Scribe Duties (40%)
• Project (40%)

Participation

We appreciate everyone being actively involved in the class! There are several ways of earning participation credit:

• In-Class quiz: In several random classes, we will ask the audiences to complete some quiz. Completing all of them is worth 10%.
• Completing mid-semester evaluation: Around the middle of the semester, we will send out a survey to help us understand how the course is going, and how we can improve. Completing it is worth 4%.
• Machine Learning seminar: There will be several ML invited talks in the semester, which will be anounced in Canvas. Attending three of them worth 6%. )

Scribe Duties

Each student is required to scribe for a certain number of lectures. Most lectures will have 2 students acting as scribes, and they should work as a team. During your assigned lectures, you are to take detailed notes in collaboration with your fellow scribes. After the lecture, the scribe team is to convert their notes into a written format. You can download the scribing template here or on overleaf. The notes must be detailed and thorough, and must be submitted on Canvas within 1 week after the lecture. TAs will audit and review the submitted notes, request changes if necessary, and will eventually approve the notes and add them to the course page.

As long as your scribe notes are of sufficient standard, you will be awarded full credit for scribe duties. If your notes have errors or are otherwise not up to standard, we will inform you and give you a chance to correct them. You will receive zero credit if you fail to submit your notes.

The scribing assignment is on this spreadsheet.

The students are required to typeset homework solutions using \(\LaTeX\) and the provided template. We recommend referring to the LaTex wikipedia to edit the notes.

Regrade Policy

If you feel that we have made a mistake in grading your homework, please submit a regrading request on Gradescope and we will consider your request. Please note that regrading of a homework may cause your grade to go either up or down.

Final Project

The class project will be carried out in groups of 2 to 4 people, and has three main parts: a proposal, a final report, and a oral presentation. The project is an integral part of this class, and is designed to be as similar as possible to researching and writing a conference-style paper.