DSC 140A – Probabilistic Modeling and Machine Learning


This Week

Introduction

Welcome to DSC 140A Spring 2026!

Here is how to get started:

  • Read the syllabus.
  • Plan for each day's contents: schedule.
  • Join our Campuswire message board and Gradescope with the email invitations you received earlier this week. If you didn't receive emails, you can use the access code 6000 for Campuswire and the access code E6P7BD for Gradescope.
  • Mark the following important dates/times:

    • The first lecture is on Monday, March 30 at
      • 12:00 PM in SOLIS 107.
    • The first discussion is:
      • Monday, March 30 3:00 PM in Catalyst 0125.
    • This quarter, the exams will be on:
      • Midterm: Friday, May 08
      • Final: Wednesday, June 10
    • Note that the midterm will be held during the regular lecture times.
    • We plan to have quizzes during discussion sections. The first quiz will be in the second week of discussion, and the quizzes will be held approximately every other week. The current schedules are:
      • Quiz 01: Monday, April 06
      • Quiz 02: Monday, April 20
      • Quiz 03: Monday, April 27
      • Quiz 04: Monday, May 18
  • Try the math self-check below to brush up on the background math.

See you in lecture!

Math Self-Check

DSC 140A builds on a lot of previous math courses. To help you brush up, we've put together an (optional) self-check, consisting of a few short problems that use some of the math facts that you may have forgotten.

We recommend following the instructions at the top of the worksheet. Namely, try the problems first, then check your answers. If you get a problem wrong, find the relevant part of the slides to review the concept.

The math check is optional and will not be turned in.

Lecture 1 — Introduction: Nearest Neighbors

Lecture 2 — Neural Networks

  • 🎞️ Slides Not yet posted...

Homework 1

  • Not yet posted...
Due Wednesday, Apr 08 at 23:59 PM

Discussion 1

future weeks

Week 2

Gradient Descent

Week 3

Linear Classification

Week 4

SVMs

Week 5

Regularization

Week 6

Kernels & Probabilistic Models

Midterm 01 on Friday, May 08

Week 7

Density Estimation

Week 8

Naive Bayes

Week 9

Regression Revisited

Week 10

Decision Trees