CSCI 378: Deep Learning


This course is an introduction to deep neural architectures and their training. Beginning with the fundamentals of regression, optimization, and regularization, the course will then survey a variety of architectures and their associated applications. Students will develop projects that implement deep learning systems to perform various tasks. Prerequisites: Mathematics 201, 202, and Computer Science 221. 


Professor: Mark Hopkins,

Class Schedule: MWF 3:10-4pm, Physics 240A

Office Hours: MW 4:10-6pm, Th 10am-noon in Library 314 (M 4:10-5 and Th 10-11am are reserved for this class; the remainder are shared with CSCI 121).

Textbook (optional): Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

Syllabus: downloadable here


  1. Machine Learning: A Whirlwind Guide (pdf)
  2. Gradient Descent (pdf)
  3. Gradient Descent: Now in 2D! (pdf)
  4. A Quick Review of Basic Probability Formulas (pdf)
  5. Bayesian Networks (pdf)
  6. How to Read a Bayesian Network (pdf)
  7. Functional Causal Models (pdf)
  8. Exponential Distributions (pdf)
  9. Regression Problems (pdf)



  1. Introduction to Tensors (Wednesday, Jan. 30):
  2. Solution to Rubik (Monday, Feb. 4):



  1. HW1 (“Memoize”, due Friday, Feb. 1):
  2. HW2 (“Rubik”, due Monday, Feb. 4):
  3. Project 1 (“Descent”, due Friday, Feb. 8):
  4. Project 2 (“Lines”, due Friday, Mar. 1):



Jan 28: Introduction

Jan 30: Class Infrastructure/Introduction to Torch

  • Reading: None
  • Assignment (due Friday, Feb. 1): HW1 (link above, under Homework Links).

Feb 1: A Whirlwind Guide to Machine Learning

  • Reading: None
  • Assignment (due Monday, Feb. 4): HW2 (link above, under Homework Links).

Feb 4: Gradient Descent

  • Reading: Review Machine Learning: A Whirlwind Guide lecture notes.
  • Assignment (suggested): Find out where in the world Chekhov’s Sun might be (i.e. complete Descent1.ipynb).

Feb 6: Gradient Descent

  • Reading: Review first part of Gradient Descent lecture notes.
  • Optional Reading: Goodfellow, Chapter 8.3 and 8.5
  • Assignment: Project 1 (link above, under Homework and Project Links)

Feb 8: Bayesian Networks

Feb 11: Reading a Bayesian Network

Feb 13: D-Separation/Causal Models

Feb 15: Causal Models

Feb 18: Exponential Distributions

  • Optional Reading: Goodfellow, Chapter 3.9.3 and 3.9.4
  • Assignment: Complete handout distributed in class.

Feb 20: Regression Problems

Feb 22: Linear Regression and Maximum Likelihood

  • Reading: Review lecture notes on Regression Problems.
  • Optional Reading: Goodfellow Chapter 5.1, 5.5