Washington University in St. Louis
Department of Computer Science and Engineering

CSE 417T: Introduction to Machine Learning

Fall 2018



This course is an introduction to machine learning, focusing on supervised learning. We will cover the mathematical foundations of learning as well as a number of important techniques for classification and regression, including linear and logistic regression, neural networks, nearest neighbor techniques, kernel methods, decision trees, and ensemble methods. Note that the material in this course is a prerequisite for CSE 517A, the graduate level machine learning class. The overlap with CSE 511A (Artificial Intelligence) is minimal.


Section Instructor Email Time Office Hours
1 Henry Chai hchai at wustl dot edu Tue/Thu 2:30-4:00PM Fri 10:00-11:00AM Jolley 224
2 Chien-Ju Ho chienju.ho at wustl dot edu Mon/Wed 2:30-4:00PM Thu 2:00-3:00PM Jolley 510
3 Bradley Flynn bflynn at wustl dot edu Tue 6:00-9:00PM

Graduate Assistants and TAs:
There are several graduate assistants and undergraduate TAs for the class. All assistants will hold regular office hours, answer questions on Piazza, and grade homeworks. The graduate assistants will also hold occasional recitation or review sessions.
TA Email Office Hours
Arghya Datta (Graduate Assistant to the Instructors) arghya at wustl dot edu Mon 6:00-8:00PM Jolley 224
Adam Kern adam.kern at wustl dot edu Mon 11:00AM-1:00PM Jolley 431
Chunyuan Li chunyuan at wustl dot edu Thu 10:00AM-12:00PM Jolley 224
Daniel Teich dteich at wustl dot edu Tue 12:00-2:00PM Jolley 224
Jack Grundy jmgrundy at wustl dot edu Thu 6:00-8:00PM Jolley 224
Ryun Han r.han at wustl dot edu Wed 1:00-3:00PM Jolley 224
Trevor Larsen trevorlarsen at wustl dot edu Fri 11:00AM-1:00PM Jolley 517
Zachary Mekus zmekus at wustl dot edu Wed 10:00AM-12:00PM Jolley 431

Office Hours
We will announce the office hours during the first two weeks of the semester.


Detailed policies are in the official syllabus. A few points to highlight: please read and understand the collaboration policy and the late day policy. There will be two exams, each covering approximately half the course material, and no separate final exam.


The main course textbook is: We also plan to cover some sections of the following book:


CSE 247, ESE 326 (or Math 320), Math 233, and Math 309 (can be taken concurrently) or equivalents. If you do not have a solid background in calculus, probability, and computer science through a class in data structures and algorithms then you may have a hard time in this class. Matrix algebra will be used and is fundamental to modern machine learning, but it's OK to take that class concurrently.


Date Topics Readings/Slides Assignments
Aug 27/28 Introduction. Course policies. Course overview. Perceptron learning algorithm. AML 1.1, 1.2; Lecture Slides (Chai) (Annotated). Lecture Slides (Ho).
Aug 29/30 Generalizing outside the training set, Hoeffding's inequality, and multiple hypotheses. AML 1.3; Lecture Slides (Annotated) hw1
Submission instructions.
Sep 4/5 Matlab Tutorial, error and noise AML 1.4; Lecture Slides (Annotated)
Sep 6/10 Infinite hypothesis spaces, growth functions AML 2.1.1; Lecture Slides (Annotated)
Sep 11/12 VC-dimension AML 2.1.1-2.1.3; Lecture Slides (Annotated)
Sep 13/17 VC-dimension, bias-variance tradeoff AML Rest of Chapter 2; Lecture Slides (Annotated) hw2
Sep 18/19 The pocket algorithm, linear regression. AML 3.1-3.2; Lecture Slides (Annotated)
Sep 20/24 Logistic regression, gradient descent. AML 3.3; Lecture Slides (Annotated)
Sep 25/26 (Stochastic) Gradient descent, nonlinear transformations. AML 3.3-3.4; Lecture Slides (Annotated)
Sep 27/Oct 1 Overfitting. AML 4.1; Lecture Slides (Annotated), Malik Magdon-Ismail's slides on overfitting
Oct 2/3 Review Lecture Slides hw3
Oct 8/9 Regularization AML 4.2; Lecture Slides (Annotated)
Oct 10/11 Validation AML 4.3; Lecture Slides (Annotated)
Oct 17/18 Three Learning Principles AML 5; Lecture Slides (Annotated)
Oct 22/23 Decision Trees and ID3 Tom Mitchell, Machine Learning Ch3; CASI 8.4; Lecture Slides (Annotated) hw4
Oct 24/25 Bagging. Random Forest. CASI 17.1; Lecture Slides (Annotated)
Oct 29/30 Boosting. AdaBoost. Freund & Schapire's Tutorial. CASI 17.4; Lecture Slides (Annotated) hw5
Oct 31/Nov 1 Nearest Neighbor. AML eChapter 6.1-6.2.1; Lecture Slides (Annotated)
Nov 5/6 Efficiency of kNN. AML eChapter 6.2-6.3; Lecture Slides (Annotated)
Nov 7/8 Radial Basis Functions (RBFs). Support Vector Machines (SVMs) AML eChapter 8.1; Lecture Slides (Annotated)
Nov 12/13 Dual SVMs AML eChapter 8.2, 8.4; Lecture Slides (Annotated)
Nov 14/15 The Kernel Trick AML eChapter 8.3; Lecture Slides (Annotated) hw6
Nov 20 Gaussian Processes (Optional) GPML Chapter 2; Lecture Slides
Nov 19/27 Neural Networks AML eChapter 7.1, 7.2.1; Lecture Slides (Annotated)
Nov 28/29 Backpropagation AML 7.2.2, 7.2.3; Lecture Slides (Annotated)
Dec 3/4 Final Review Final Review