Machine Learning, Fall 2018






Time/Location: Tue/Thu 2:00-3:15 pm in room CAS B12

Course Number: CS 542

Instructor: Kate Saenko,; office hours: T/Th 3:30-5pm in MCS-296

Teaching Fellows: Fred Fung ( office hours: EMA 302 T 5-6PM, W 2:25PM to 3:25PM, Xingchao Peng ( office hours: EMA 302 M: 5-6 PM, Th:5-6PM

Graders: Sid Mysore, Ben Usman, Vitali Petsiuk







Tue Sep 4

Course Introduction

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

Wed lab

Probability and Math Review

background knowledge on linear algebra and probability theory. Useful reference on matrix calculus; also see

Thu Sep 6

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

ps0 out

Tue Sep 11

Supervised Learning II: Classification

(guest lecture by Prof. Kulis)

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

Wed lab

Multivariate Gaussian Review, Eigenvectors, ps0

Thu Sep 13

Intro to Projects

project pitches from BUSpark! partners

ps0 due (11:55am Fri)

ps1 out

Tue Sep 18

Supervised Learning III: Regularization

more logistic regression, regularization; bias-variance Reading: Bishop 3.2; 3.1.4

Wed lab

ps0 and Numpy Tutorial

Thu Sep 20

Unsupervised Learning I: Clustering

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

ps1 due (11:55am Fri)

ps2 out

Tue Sep 25

Unsupervised Learning II: PCA

dimensionality reduction, PCA. Reading: Bishop 12.1

Wed lab

ps1 Solution & ps2 Hints


Thu Sep 27

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 (11:55am Fri)

ps3 out

project signup  due (11:55am Fri)

Tue Oct 2

Neural Networks II: Learning

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

teams assigned LINK

Wed lab

TensorFlow Tutorial

Build a NN to classify iris.

Thu Oct 4

Neural Networks III: Convolutional Nets

Convolutional networks. Reading: Bishop Ch 5.5

ps3 due (11:55am Fri)

ps4 out

Tue Oct 9


Wed lab

Convolutional Nets demo

Thu Oct 11

Neural Networks IV: Recurrent Nets

recurrent networks; training strategies

ps4 due (11:55am Fri)

Tue Oct 16

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

Wed lab

Midterm Review

Thu Oct 18


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

Midterm Practice Problems



Tue Oct 23

Probabilistic Generative Models

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

Wed lab

Thu Oct 25

Bayesian Methods

priors over parameters; Bayesian linear regression;     

Reading: Bishop Ch 2.3

ps5 out

Tue Oct 30

Support Vector Machines I

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

Wed lab

Thu Nov 1

Support Vector Machines II

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

project proposal due (in class)

ps5 due (11:55am Fri)

ps6 out

Tue Nov 6

Support Vector Machines III

Dual formulation; kernels; multiclass SVM; Reading: Bishop Ch 6.1-6.2, Ch 7.1.3

Wed lab

Evaluation Metrics for ML

Thu Nov 8

Unsupervised Learning III: Anomaly Detection

Density estimation for anomaly detection; evaluating anomaly detection

ps6 due (11:55am Fri)

Tue Nov 13

Unsupervised Learning IV: GANs

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

Wed lab

project help

Thu Nov 15

Reinforcement Learning I

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

project update I due (in class);

Tue Nov 20

Reinforcement Learning II

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

Wed lab


Thu Nov 22



Tue Nov 27

Domain Adaptation for Visual Data

domain shift; domain adaptation; adversarial feature alignment

Wed lab

project help

Thu Nov 29

Language and Vision Applications

Image captioning, video captioning, visual question answering

project updateII due(in class)

Fri Nov 30 self-grading due

Tue Dec 4

Bias and Fairness in Machine Learning

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

Wed lab

Final Review

Thu Dec 6

Final Review

submit a course evaluation at

Tue Dec 11

Project presentations

poster session 1:00-3:00pm in Hariri (there is another poster session starting right after so please take your posters down promptly)

project due Tue 11:55pm submission instructions

Submit Here

Tue Dec 18

Final exam 3:00pm-5:00pm CAS B12

covers everything up to and including Reinforcement Learning II; 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, and Bayesian methods. Weekly labs and projects emphasize taking theory into practice, through applications on real-world problems and data sets.

Course Pre-requisites

This is an upper-level undergraduate/intro graduate course and requires the following skills:


The required textbook for the course is

Other recommended supplemental textbooks on general machine learning:

Recommended background reading on matrix calculus:

Recommended online courses

Deliverables/Graded Work

The main graded work for the course is the midterm, final and project. There will also be six self-graded homework assignments, each consisting of written and programming problems, which are meant to prepare students for the two exams. The project will be done in teams of 4 students and will have several deliverables including a proposal, progress update(s), code, report a final in-class presentation. The course grade consists of the following:


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


Students will apply their knowledge of machine learning to practical projects provided by local companies/nonprofits in collaboration with BU Spark! Teams will be able to choose from several projects and will interact with mentors from the partner institution as they develop their machine learning solution. The projects will culminate with a poster presentation at the end of term.

Project expectations:

See the Projects page for more details.


Programming assignments will be developed in the Python programming language. You may use other languages for the projects, 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, you should stop by the CS undergraduate lab (EMA 302) and activate one.  This process takes only a few minutes, and can be done at any time during the lab's operating hours: <>

Late Policy

Late work will incur the following penalties

Academic Honesty Policy

The instructors take academic honesty very seriously. Cheating, plagiarism and other misconduct may 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.

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.