Machine Learning, Fall 2019




Course Policies

Time/Location: Tue/Thu 2:00-3:15 pm in room LSE B01

Course Number: CS 542

Instructor: Kate Saenko ( office hours: T/Th 3:30-5pm in MCS-200

Teaching Fellows: 

    Ben Usman ( office hours: Wed 11:15-12:15pm and 14:25-15:25pm in EMA 302  

    Vasili Ramanishka ( office hours: Tue 5:00-7:00pm in EMA 302

Graders: Runqi Tian (, Ximeng Sun (, Andrea Burns (, Chi Zhang (

Please only use email for personal questions, e.g. grading, use Piazza for all other questions:

Piazza: discussion forum and problem sets:

Who should take this class?

This is a difficult, math- and programming-intensive class geared primarily towards graduate students. You should be very comfortable with matrix algebra, calculus, partial derivatives, probability densities and other material listed in the prerequisites. In addition, you must be very comfortable with programming in Python, and be able to both write and understand code quickly. If you lack any of these prerequisites, you SHOULD NOT take this class expecting that the instructors will teach you all of that material plus the course content. We are not miracle workers! Instead, please consider these alternatives.






Tue Sep 3


what is machine learning? types of learning; features; hypothesis; cost function; course information

Wed lab

LAB1: Probability and Math Review

Thu Sep 5


review of expected mathematical skills for the course; Useful reference on matrix calculus; also see

ps0 (public)

ps0 (piazza) (math prerequisites)

Tue Sep 10

Supervised Learning I: Regression

regression, linear hypothesis, SSD cost; gradient descent; normal equations; maximum likelihood; Reading: Bishop 1.2-1.2.4,3.1-3.1.1

Wed lab

LAB2: Machine Learning Overview

Thu Sep 12

Supervised Learning II: Classification

classification; sigmoid function; logistic regression. Reading: 4.3.1-4.3.2; 4.3.4

overview of logistic regression

ps0 due

submit solution (11:59pm)

ps1 out

Tue Sep 17

Supervised Learning III: Regularization

more logistic regression, regularization; bias-variance Reading: Bishop 3.1, 3.2

Wed lab

LAB3: Linear Regression

Thu Sep 19

Unsupervised Learning I: Clustering

clustering, k-means, Gaussian mixtures. Reading: Bishop 9.1-9.2

ps1 due (11:59pm)

ps2 out

Tue Sep 24

Unsupervised Learning II: PCA

dimensionality reduction, PCA. Reading: Bishop 12.1

Wed lab

LAB4: Logistic Regression


Thu Sep 26

Neural Networks I: Feed-forward Nets

artificial neuron, MLP, sigmoid units; neuroscience inspiration; output vs hidden layers; linear vs nonlinear networks; feed-forward neural networks; Reading: Bishop Ch 5.1-5.3

ps2 due (11:59pm)

ps3 out

Tue Oct 1

Neural Networks II: Learning

Learning via gradient descent; computation graphs, backpropagation algorithm. Reading: Bishop Ch 5.1-5.3

Wed lab

LAB5: Gaussian Mixture Models

Thu Oct 3

Neural Networks III: Convolutional Nets

Convolutional networks. Reading: Bishop Ch 5.5; Goodfellow et al Ch. 9. (optional)

ps3 due (11:59pm)

ps4 out

Tue Oct 8

Neural Networks IV: Recurrent Nets

recurrent networks; training strategies

Wed lab

LAB6: Backprop

Thu Oct 10

Computing cluster/Tensorflow Intro

(guest lecture by Katia Oleinik)

Intro to SCC and Tensorflow; please bring laptops to class to follow along with the lecture.

Also see Software/Hardware below. 

ps4 due (11:59pm)

Tue Oct 15


Wed lab

LAB7: Midterm Review

Thu Oct 17


(closed book, no electronics, you may bring a single 8“x11” sheet of paper with typed or handwritten notes on both sides)

covers everything up to and including Neural Networks IV; expect questions on material covered in lectures, problem sets, LABs and assigned reading

Midterm Practice Problems



Tue Oct 22

Probabilistic Models I: LDA

generalized linear models; generative vs discriminative models; linear discriminant analysis (LDA); Reading: Bishop Ch 4.2

Wed lab

LAB8: Midterm Review

Thu Oct 24

Probabilistic Models II: Bayesian Methods

priors over parameters; Bayesian linear regression;     

Reading: Bishop Ch 2.3

ps5 out

Tue Oct 29

Support Vector Machines I

(guest lecture by Prof. Sarah Bargal)

maximum margin methods; support vector machines; primal vs dual SVM formulation; Hinge loss vs. cross-entropy loss; Reading: Bishop Ch 7.1.1-7.1.2

Wed lab

LAB9: LDA, SVM, Kernels

Thu Oct 31


ps5 due (11:59pm)

ps6 out

Tue Nov 5

Support Vector Machines II

non-separable data; slack variables;kernels; multiclass SVM; Reading: Bishop Ch 6.1-6.2, Ch 7.1.3

Wed lab


Thu Nov 7

Reinforcement Learning I

reinforcement learning; Markov Decision Process (MDP); policies, value functions, Q-learning

Tue Nov 12

Reinforcement Learning II

Q-learning cont’d; deep Q-learning (DQN)

ps6 due (11:59pm)

ps7 out

Wed lab


Thu Nov 14

Unsupervised Learning III: Anomaly Detection

Anomaly detection methods: density estimation, reconstruction based method, One Class SVM; evaluating anomaly detection

Tue Nov 19

Unsupervised Learning IV: Generative Adversarial Networks (GANs)

Implicit generative models; adversarial methods; Generative Adversarial Nets (GANs); Reading: Goodfellow et al. NIPS 2014

ps7 due (11:59pm)

Challenge starts

Wed lab

LAB12: RL | Challenge

Thu Nov 21

Unsupervised Learning V: Semi-supervised Learning

Semi-supervised learning (SSL); self-training; co-training; clustering methods, SSSVM

Deadline to register for challenge (11:59pm)


Tue Nov 26

Practical Advice for Applying ML

Machine learning system design; feature engineering; feature pre-processing; learning with large datasets; SGD and mini-batch GD

Deadline to register for challenge (11:59pm)

Wed lab


Thu Nov 28


Tue Dec 3

Applications II: Language and Vision

Image and video captioning, visual question answering, other V&L understanding problems

Submit something to leaderboard!

Wed lab

LAB13: Challenge Discussion

Thu Dec 5

Applications II: Machine Learning Ethics

Ethics in ML; population bias in machine learning, fairness, transparency, accountability; de-biasing image captioning models

Tue Dec 10

Final Review

 submit a course evaluation at

Challenge ends Tue Dec 10 12:00pm (NOON)

Code due Wed Dec 11 12:00pm 

Tue Dec 17

Final exam

3:00PM - 5:00PM in LSE B01

(closed book, no electronics, you may bring a single 8“x11” sheet of paper with typed or handwritten notes on both sides)

expect questions on material covered in the entire course in lectures, problem sets, LABs, and assigned reading

Additional practice problems


*schedule is tentative and is subject to change.


This course is an introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, classification, unsupervised and supervised learning, kernels, support vector machines, feature selection, clustering, sequence models, Bayesian methods, and more. Weekly labs and problem sets emphasize taking theory into practice, by gaining a thorough mathematical understanding of the machine learning methods and coding them to apply them to data sets.

Course Pre-requisites

This is an introductory graduate course (open to upper-level undergraduates) and requires the following:

In addition, either Foundations of Data Science (CS391 E1) or Intro to Optimization (CAS CS 507) are highly recommended as a precursor to this course.


The required textbook for the course is

Other recommended supplemental textbooks on general machine learning:

Recommended background reading on matrix calculus:

Alternative Machine Learning Courses

Deliverables/Graded Work

The main graded work for the course is the midterm, final, problem sets and class challenge. There will be eight self-graded weekly problem sets, each consisting of written and programming problems, which are meant to prepare students for the two exams. (Note: there are no team projects in this course). The course grade consists of the following:

Class Challenge

The class challenge is a Kaggle-style individual competition that gives each student a chance to apply their knowledge to a real-world problem, developing a solution from start to finish. More information about the challenge will be released later on in the term.


We will be using Piazza for class discussion and posting problem sets. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and instructor. Rather than emailing questions to the teaching staff, we encourage you to post your questions on Piazza. If you have any problems or feedback for the developers of the platform, email


Programming assignments will be developed in the Python programming language, using iPython notebooks. If you do not already have a CS account and would like one, please visit

For the challenge, students will have access the SCC computing cluster which has GPU and CPU nodes available. See the notes from Intro to SCC and Tensorflow for further information on using the SCC. At the end of the notes there are a number of links, including cheat sheets, links to the documentation on how to submit jobs and an examples webpage. Note: there are directions on how to open a Jupyter notebook using port forwarding, however, is now much easier to do this using "SCI OnDemand". You can access it by typing "" in the browser. Also: you should not worry about SUs for this SCC project, but be careful not to use up too much space (you can check how much space is used with the pquota command).

Another resource you may want to use for some problem sets or the challenge is Google’s Colab, see


Late Policy

Late work will incur the following penalties

The lowest of all the problem set grades will be dropped. Exceptions to these policies can only be made in cases of significant medical or family emergencies, and should be documented.

Academic Honesty Policy

The instructors take academic honesty very seriously. Cheating, plagiarism and other misconduct will be subject to grading penalties up to failing the course.  Students enrolled in the course are responsible for familiarizing themselves with the detailed BU policy, available here. In particular, plagiarism is defined as follows, and applies to all written materials and software, including material found online. Collaboration on homework is allowed, but should be acknowledged and you should always come up with your own solution rather than copying (which is defined as plagiarism):

Plagiarism: Representing the work of another as one’s own. Plagiarism includes but is not limited to the following: copying the answers of another student on an examination, copying or restating the work or ideas of another person or persons in any oral or written work (printed or electronic) without citing the appropriate source, and collaborating with someone else in an academic endeavor without acknowledging his or her contribution. Plagiarism can consist of acts of commission-appropriating the words or ideas of another-or omission failing to acknowledge/document/credit the source or creator of words or ideas (see below for a detailed definition of plagiarism). It also includes colluding with someone else in an academic endeavor without acknowledging his or her contribution, using audio or video footage that comes from another source (including work done by another student) without permission and acknowledgement of that source.

Prohibited behaviors include:

Incidents of academic misconduct will be reported to the Academic Conduct Committee (ACC). The ACC may suspend/expel students found guilty of misconduct. At a minimum, students who engage in misconduct will have their final grade reduced by one letter grade (e.g., from a B to a C).

Religious Observance

Students are permitted to be absent from class, including classes involving examinations, labs, excursions, and other special events, for purposes of religious observance.  In-class, take-home and lab assignments, and other work shall be made up in consultation with the student’s instructors. More details on BU’s religious observance policy are available here.