Northwestern University CS349: Machine Learning, Fall 2021

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Course Description

Machine learning is the study of algorithms that improve automatically through experience. This is an overview class that covers the fundamentals: classification, regression, and model training and evaluation across a variety of approaches, both old and new.


For this course, it is extremely helpful to be familiar with probability, statistics, python programming and matrix math. We expect students to come prepared with those skills. Here are some prerequisite materials to get up to speed, in case you need a refresher.

Class Meeting Time & Place

Section 1: Mon Wed 9:30AM - 10:50AM Frances Searle Building 2107
Section 2: Tue Thu 8:00AM - 9:20AM Harris Hall 107
Videos lectures will be available on Canvas.

Office Hours

These hours are subject to change depending on demand. A calendar view of these office hours is available here.


Bryan Pardo: Tue Thu 9:40am - 11:00am via Zoom Go here to book a 10 minute meeting.

Zach Wood-Doughty: Mon Fri 1:30-3pm via Zoom

Teaching Assistants

Hugo Flores Garcia: Tue Thu @ 5-6pm via Zoom

Patrick O’Reilly: Sat 2-4pm via Zoom

Peer Mentors

Alex Tai: Sun 9-11am in Tech EG20

Boaz Cogan: Mon Wed 8-9am in Tech EG20

Noah Schaffer: Fri 9-11am in Tech EG20

Aldo Aguilar: Mon Wed 5-6pm via Zoom

Garphy Tam: Tue Wed 3-4pm via Zoom

Simran Gadkari: Tue Fri 12-1pm via Zoom

Utkarsh Mishra: Wed Thu 6-7pm via Zoom


Course Calendar

This is a prediction of what will be covered in each week but the schedule is subject to change as the course progresses.

Week Due Topic (click on topic for slides) Lecturer
09/20   No class  
    Intro to ML Pardo
09/27 HW 0 P-norms and Distance Measures Pardo
    Nearest Neighbor Classifiers Pardo
10/04   Linear and Polynomial Regression Wood-Doughty
    Linear Discriminants Wood-Doughty
10/11 HW 1 Decision trees Pardo
    Measuring Error Pardo
10/18   Gradient Descent Pardo
    Multilayer Perceptrons and Backprop Pardo
10/25 HW 2 Convolutional Networks Pardo
    Convolutional networks continued Pardo
11/01   Clustering Wood-Doughty
    Expectation Maximization Wood-Doughty
11/08 HW 3 Graphical Models Wood-Doughty
    Graphical Models (continued) Wood-Doughty
11/15   Hidden Markov Models Wood-Doughty
    Causal Graphical Models Wood-Doughty
11/22 HW 4 Reinforcement Learning Pardo
11/29   Fairness, accountability, transparency, and ethics Wood-Doughty
12/01   Current research topics in ML O’Reilly
12/02   Current research topics in ML Garcia
12/08 HW 5 NO CLASS: Finals week  


Helpful Reading









Lectures Slides

The lecture slides will be made available on the course calendar above, as the course progresses.

Lecture Videos

Videos of the Tuesday/Thursday lectures will be made available on Canvas (via Panopto) on the same day they are recorded.

  1. CS229 at Stanford
  2. Beautiful Cheetsheet on supervised Machine Learning
  3. Lecture Notes by Miguel A. Carreira-Perpinan
  4. Well curated course notes (and slides) by Sebastian Raschka. Go here for the intro pdf
  5. Andrew Ng - Lecture Notes and from someone that wrote alternate notes based on these lectures


People who contributed to the design of this course and the homeworks:

Bryan Pardo, Prem Seetharaman, Bongjun Kim, Max Morrison, Ethan Manilow, Fatemeh Pishdadian, Lukas Justen, Oliver Coissart, Florian Schiffers, Sushobhan Ghosh, Ze Zhu, Zach Wood-Doughty, Hugo Flores Garcia, Patrick O’Reilly

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