CMPSCI 390A: Introduction to Machine Learning

Spring 2021, University of Massachusetts

Lecture Times: Tuesdays and Thursdays, 11:30am-12:45pm Eastern

Course Information


Download Syllabus .pdf

Lecture:11:30am-12:45pm Tuesdays and Thursdays

Zoom link: https://umass-amherst.zoom.us/j/94525449604


Description

The course provides an introduction to machine learning algorithms and applications. Machine learning algorithms answer the question: "How can a computer improve its performance based on data and from its own experience?" The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness. Specific topics include linear and non-linear regression, (stochastic) gradient descent, neural networks, backpropagation, classification, Markov decision processes, state-value and action-value functions, temporal difference learning, actor-critic algorithms, the reward prediction error hypothesis for dopamine, connectionism for philosophy of mind, and ethics, safety, and fairness considerations when applying machine learning to real-world problems.


Download Course Notes .pdf

TAs and Office Hours


The TAs for this course are Cooper Sigrist (csigrist@umass.edu) and Scott Jordan (sjordan@cs.umass.edu). Scott will primarily be handling assignments and grading, and so you should ask him questions related to grading. Cooper will primarily be holding office hours.

Office hours will be at the following times:

Day Time Person Link
Monday 8:00am-9:45am Cooper Sigrist link
Tuesday 1:00pm-3:00pm Cooper Sigrist link
Wednesday 4:00pm-6:00pm Philip Thomas link
Thursday 8:00am-9:45am Cooper Sigrist link
Friday 1:00pm-3:00pm Cooper Sigrist link

Office hours will follow the UMass Academic Calendar [link]. For example, Monday March 1 will follow a Wednesday schedule, and so Philip Thomas will be holding office hours and Cooper Sigrist will not. Office hours will run up to and including the last day of classes, May 4.

Assignments


  1. Homework 1 has been assigned on 2 February 2021 and is due at 11:00am on 4 February 2021. [lin (.pdf)]
  2. Homework 2 has been assigned on 8 February 2021 and is due at 11:00am on 11 February 2021. [link (.zip)]
  3. Homework 3 has been assigned on 11 February 2021 and is due at 11:00am on 18 February 2021. [link (.zip)]
  4. Homework 4 has been assigned on 19 February 2021 and is due at 11:00am on 25 February 2021. [link (.zip)]

Schedule


Part I: Supervised Learning

Lecture Topic Reading Whiteboard
1 Introduction Chapter 1 (course notes) link
2 Regression, k-Nearest Neighbors, Linear Regression I Chapter 2 (course notes) link
3 Linear Regression II Chapter 3 link
4 Linear Regression III, Gradient Descent Chapter 4 link
5 Gradient Descent (continued) Chapter 5 link
6 Basis functions, feature normalization, perceptrons Chapter 6 link
7 Backpropagation link
8 Classification, overfitting, train-test splits

Part II: Reinforcement Learning

Lecture Topic Reading Slides
9 Introduction
10 Markov decision processes
11 Value functions
12 Bellman equations
13 Temporal difference learning
14 Function approximation
15 Actor-critic algorithms
16 Reinforcement learning conclusion

Part III: Ethics, Safety, Fairness, and Connections to other Areas

Lecture Topic Reading Slides
17 Connections to psychology
18 Connections to neuroscience
19 Introduction to philosophy of mind
20 Connectionism and connections to philosophy of mind
21 Safety and fairness Part 1
22 Safety and fairness Part 2
23 Ethics Part 1
24 Ethics Part 2

Part IV: Conclusion

Lecture Topic Reading Slides
25 Survey of other topics
26 Conclusion and review