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-296

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: Multivariate Gaussian Review, Eigenvectors

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 3.2; 3.1.4

Wed lab

LAB3: ps0 and Numpy Tutorial

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: ps1 Solution & ps2 Hints


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; backpropagation algorithm. Reading: Bishop Ch 5.1-5.3

Wed lab

LAB5: TensorFlow Tutorial

Thu Oct 3

Neural Networks III: Convolutional Nets

Convolutional networks. Reading: Bishop Ch 5.5

ps3 due (11:59pm)

ps4 out

Tue Oct 8

Neural Networks IV: Recurrent Nets

recurrent networks; training strategies

Wed lab

LAB6: Convolutional Nets demo

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 and install software according to these instructions

ps4 due (11:59pm)

Tue Oct 15


Wed lab

LAB7: Midterm Review

Thu Oct 17


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; Reading: Bishop Ch 4.2

Wed lab


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

hinge loss, maximum margin method; support vector machines; Reading: Bishop Ch 7.1.1-7.1.2

Wed lab


Thu Oct 31

Support Vector Machines II

Hinge loss vs. cross-entropy loss; primal SVM formulation; non-separable data; slack variables;

ps5 due (11:59pm)

ps6 out

Tue Nov 5

Support Vector Machines III

Dual formulation; 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

ps6 due (11:59pm)

ps7 out

Tue Nov 12

Reinforcement Learning II

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

Wed lab


Thu Nov 14

Unsupervised Learning III: Density Estimation

Density estimation for anomaly detection; evaluating anomaly detection

ps7 due (11:59pm)

Challenge starts

Tue Nov 19

Unsupervised Learning IV: GANs

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

Wed lab


Thu Nov 21

Unsupervised Learning V: Domain Adaptation

domain shift; domain adaptation; adversarial feature alignment


Tue Nov 26

Applications I: Language and Vision

Image captioning, video captioning, visual question answering

Wed lab


Thu Nov 28


Tue Dec 3

Applications II: Bias and Fairness in Machine Learning

Bias in machine learning, fairness, transparency, accountability; de-biasing image captioning models

Wed lab

LAB13: Final Review

Thu Dec 5


submit a course evaluation at

Tue Dec 10

Final Review

Challenge ends


Final exam

expect questions on material covered 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 intro graduate course/upper-level undergraduate 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. The class challenge is a Kaggle-style competition that gives each student a chance to apply their knowledge to a real-world problem, developing a solution from start to finish (Note: there are no team projects in this course). The course grade consists of the following:


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. You may use other languages for the challenge, but note that the course staff may not be able to help answer questions specific to certain languages. If you do not already have a CS account and would like one, please visit


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.