DSC 140A – Probabilistic Modeling and Machine Learning


This Week

Gradient Descent

Lecture 3 — Gradient Descent

Lecture 4 — SGD and Subgradients

Lab 2

Due Sunday, Jan 19 at 23:59 PM

Homework 2

Due Wednesday, Jan 22 at 23:59 PM

Discussion 2

past weeks

Week 1

Introduction

Welcome to DSC 140A!

Here is how to get started:

  • Read the syllabus.
  • 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 7229 for Campuswire and the access code DKVPD2 for Gradescope.
  • Mark the following important dates/times:
    • The first lectures are on Tuesday, January 07 at
      • 11:00 AM in CENTR 212.
      • 3:30 PM in CENTR 113.
    • The first discussions are:
      • Friday, January 10 5:00 PM in CENTR 216.
      • Monday, January 13 7:00 PM in CENTR 212.
    • This quarter, the exams will be on:
      • Midterm 01: Thursday, February 06
      • Midterm 02: Thursday, March 06
      • Redemption Exams: Saturday, March 15
    • Note that the midterms will be held during the regular lecture times (despite what is shown in the official class schedule).
  • 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 — ERM and Least Squares

Lab 1

Was due Sunday, Jan 12 at 23:59 PM

Homework 1

Was due Wednesday, Jan 15 at 23:59 PM

Discussion 1

future weeks

Week 3

Linear Classification

Week 4

SVMs

Week 5

Regularization

Midterm 01 on Thursday, Feb 06

Week 6

Kernels & Probabilistic Models

Week 7

Density Estimation

Week 8

Naive Bayes

Week 9

Regression Revisited

Midterm 02 on Thursday, Mar 06

Week 10

Decision Trees

Redemption Exams on Saturday, Mar 15