Announcements

HW6 will be posted Friday 27 May. It will be an extra credit assignment due Tuesday 31 May @ 23:59 PST. The material on this homework will appear on the final exam, so even if you do not do the hw for extra credit, you are responsible for understanding it since it will be fair game for the final.

The final exam will be posted Friday 3 June @ 8:00 PST. It will be a take home exam just like the midterm. The final will be comprehensive and cover the material from all modules. This includes homework exercises and Python/Section exercises. The final exam will include both numerical exercises in Python and handwritten exercises like the midterm. As we have promised, if you do better on the final than the midterm, we will replace your midterm score with the final exam score.

Homework 4 has been posted. This week we will continue our discussion of spectral methods. Monday we will discuss PCA and its connections to SVD and low rank approximation. Wednesday we will talk about PCR (principal component regression), KernelPCA and potentially spectral clustering depending on time. The week of 2022-05-16 we will start Module 4 on convexity and that will be the last module.

The Mod2-N3 notebook on least squares classification with MNIST has been posted.

HW1 self-assessment quiz has been posted and hw2 has also been posted. Reminder: you can find information on the self-grade process here. You must complete the self-grade process (including the quiz and commenting on your problems) to get credit for your hw.

Templates for the lecture notes will be posted before the lecture in the calendar below. We will also post completed slides after the class with either handwritten or typed content on the templates.

To prepare for the quarter, we recommend familiarizing yourself with the Syllabus to learn about how the course will run online. You can also check out the Staff Page to meet the instructional team, or the Calendar to get a preview of the topics we’ll cover. Other content such as projects and exercises will be published here as they are released during the quarter.

Course Organization¶

We’re excited to be working with you this quarter, even as we are still slugging through the pandemic working towards the endemic phase!

This website will contain all the information for the course. We will use Canvas for submitting homework and doing self-grading of homework assigmnments; gradescope will be used for exams.

This course will be taught in a traditional lecture-style and will be organized into modules.
Monday and Wednesday class sessions will be dedicated to traditional lecture with many examples throughout. Friday will be dedicated to a TA led discussion section which is focused on solving problems and working through Python notebooks.

We will strive to record all in-class sessions via zoom. Students needing to miss class due to illness will be able to login to zoom during the in-class sessions as well. We will also have a discord that we will use for discussion related to lectures and homework.

Homework¶

The class will have weekly homework starting in week 2. These homeworks will be ‘self-graded’. The purpose of self-grading is to 1. Incentivize you to review your homework solutions. I have found this to be instrumental in making sure students’ understanding of topic is solidified, and retention is improved. 2. Give you the opportunity to earn full-credit on the homework. We will select 1-2 homework problems per assignment to hand grade. More detail on self-grading can be found on the homework link.

We’re looking forward to meeting you! Please reach out to the staff if you have any questions or concerns about the quarter.