DSC 190 – Representation Learning

This Week

Finals Week

Final Exam on Monday, Jun 06

Week 10

Conclusion

Lecture 17 — Conclusions

  • 🎞️ Slides Not yet posted...

Homework 9

Was due Wednesday, Jun 08 at 23:59 PM

Week 9

CNNs

Lecture 16 — Convolutional Neural Networks

Week 8

Neural Networks II

Lecture 14 — More Backprop + Activation Functions

Lecture 15 — Deep Learning

Homework 8

Was due Wednesday, May 25 at 23:59 PM

Discussion 7

Week 7

Neural Networks I

Lecture 12 — Neural Networks

Lecture 13 — Convexity

Homework 7

Was due Wednesday, May 18 at 23:59 PM

Discussion 6

Week 6

Manifold Learning

Lecture 10 — Spectral Embeddings

Lecture 11 — Nonlinear Dimensionality Reduction

Homework 6

Was due Wednesday, May 11 at 23:59 PM

Discussion 5

Week 5

PCA

Lecture 9 — PCA, Part II

Homework 5

Was due Wednesday, May 04 at 23:59 PM

Discussion 4

Week 4

Eigenvalues and Eigenvectors

Lecture 7 — Eigenvectors

Lecture 8 — PCA, Part I

Homework 4

Was due Wednesday, Apr 27 at 23:59 PM

Discussion 3

Week 3

K-Means & RBF Networks

Lecture 5 — K-Means Clustering

Lecture 6 — What is a matrix?

Homework 3

Was due Wednesday, Apr 20 at 23:59 PM

Discussion 2

Week 2

Basis Functions

Lecture 3 — Basis Functions

Lecture 4 — RBF Networks

Homework 2

Was due Wednesday, Apr 13 at 23:59 PM

Discussion 1

Week 1

Introduction

Welcome to DSC 190 - Representation Learning!

Here is how to get started:

  • Read the syllabus.
  • Join our Campuswire and Gradescope with the email invitations you received earlier this week. If you didn't receive an email, you can use access code 4180 for Campuswire and GE4VYB for Gradescope.
  • The first lecture will be on Tuesday in MANDE B-210 at 12:30 pm. Attendance is optional, and lectures will be recorded and posted to podcasts.ucsd.edu.
  • There is no discussion in Week 01!

See you in lecture!

Lecture 1 — Introduction

Lecture 2 — Linear Predictors

Homework 1

Was due Wednesday, Apr 06 at 23:59 PM