Fall 2018 Deep Learning​: Syllabus and Schedule

Course Description:
This course is an introduction to deep learning, a branch of machine learning concerned with the development and
application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in
a way that maximizes performance on a given task. For example, asked to recognize faces, a deep neural network
may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and
ears, and, finally, individual face identities. Deep learning is behind many recent advances in AI, including Siri’s
speech recognition, Facebook’s tag suggestions and self-driving cars.

We will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep
unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computer
vision. Prerequisites: a strong mathematical background in calculus, linear algebra, and probability & statistics
(students will be required to pass a math prerequisites test), as well as programming in Python and C/C++. There will
be assignments and a final project.

Time/Location:​ Mon/Wed 2:30-4:15pm in room CAS 313

Sections​: EC500 K1 / CS591 K1
Instructor:
Brian Kulis, ​bkulis@bu.edu​; office hours: ​ Tuesday 11:0-12:30pm in PHO 441
Teaching Assistants:
Ali Siahkamari, siaa@bu.edu, ​office hours: M/W 4:30-6:30pm, CS Undergrad Lab (730 Comm Ave #302)
Xide Xia, ​xidexia@bu.edu​, office hours: M/W 4:30-6:30pm, CS Undergrad Lab
Weichao Zhou, ​zwc662@bu.edu
Mehrnoosh Sarmashghi, msmshgi@bu.edu
Blackboard: ​registered students can access via ​https://learn.bu.edu

Course Pre-requisites
This is an upper-level undergraduate/graduate course. All students should have the following skills:
■ Calculus, Linear Algebra
■ Probability & Statistics
■ Ability to code in Python
■ Background in machine learning

In addition,​ students must complete and pass the Pre-Quiz on prerequisite math knowledge​ – see schedule below.
Students who cannot pass the Pre-Quiz must drop the class.

Syllabus and Schedule Lectures Assignments Reading Videos

Schedule*
Topic (Instructor) Details Homework

Wed Sep 5 1. Course overview What is deep learning? DL successes; syllabus & course
logistics; what is on the pre-quiz?

Mon Sep Math Prerequisite Quiz there will be no make-up quiz

10

Wed Sep 2. Math review I Gradient descent, logistic regression. ​Reading​: Goodfellow return pre-quiz,
12 Ch5.9-5.10 hw1 out

Mon Sep 3. Math review II Probability, continuous and discrete distributions; maximum
17 likelihood. ​Reading:​ Goodfellow Ch5.1-5.6

Wed Sep 4. Intro to neural networks cost functions, hypotheses and tasks; training data;
19 maximum likelihood based cost, cross entropy, MSE cost;
feed-forward networks; perceptron; neuroscience
inspiration; ​Reading:​ Goodfellow Ch6.2

Mon Sep 5. SCC/TensorFlow Overview How to use the SCC cluster; introduction to Tensorflow. SCC Info
24 Please bring your laptop to class, this will be an interactive
(Katia Oleinik)
tutorial.

Wed Sep 6. Learning in neural networks output vs hidden layers; linear vs nonlinear networks; ps1 due
26 Reading:​ Goodfellow Ch6.1-6.3 11:59pm
(Kate Saenko)
Ps2 out

Mon Oct 1 7. Backpropagation learning via gradient descent; recursive chain rule
(backpropagation); if time: bias-variance tradeoff,
regularization; output units: linear, softmax; hidden units:
tanh, RELU; ​Reading​: ​backprop notes​, Goodfellow Ch6.5

Wed Oct 3 8. Deep learning strategies I GPU training, regularization,etc; project proposals

Tue Oct 9 9. Deep learning strategies II Optimization algorithms, dropout, batch normalization

Wed Oct 10 10. CNNs Convolutional neural networks; ​Reading​: Goodfellow Ps2 due
Ch9.1-9.3 11:59pm
Ps3 out

Mon Oct 15 11. Unsupervised deep learning I Autoencoders Project

proposal due

Wed Oct 17 12. Unsupervised deep learning II Generative Adversarial Networks

Mon Oct 22 13. RNNs I recurrent neural networks; sequence modeling;

backpropagation through time; vanishing/exploding gradient
problem; gradient clipping, long-short term memory (LSTM)

Wed Oct 24 14. RNNs II more intuition about RNNs, LSTMs; toy addition problem; Ps3 due
language modeling; bi-directional RNN 11:59pm
Ps4 out

Mon Oct 29 15. Deep Belief Nets I Probabilistic modeling

Wed Oct 31 16. Variational Methods Variational Autoencoders

Mon Nov 5 17. No class Brian out of town

Wed Nov 7 18. Deep Belief Nets II; Attention Applications of Deep Belief Nets and related models;
and Memory Neural Turing Machines ​Reading:​ ​Neural Turing Machines
paper; ​Neural Machine Translation by Jointly Learning to
Align and Translate​ paper;
http://distill.pub/2016/augmented-rnns/​ (optional)
 Mon Nov 19. Deep Reinforcement Learning Overview of RL
12
I

Wed Nov 20. Deep reinforcement learning Policy Gradient Ps4 due
14 11:59pm
II

Mon Nov 21. Deep Reinforcement Learning Actor-critic, Q-learning Progress report
19 due in class
III
Template

Fri Nov 23 PS5 out

Mon Nov 22. Image/Video Captioning,

26
Autonomous Driving

Wed Nov 23. Other NLP applications Parsing, recursive neural networks
28

Mon Dec 3 24. Speech and Audio ResNet and WaveNet

Applications

Wed Dec 5 No class NIPS Conference

Fri Dec 7 Ps5 due

11:59pm

Mon Dec Project presentations I project

10 presentation
(due ​12:00pm
on day of
presentation​)

Wed Dec Project Presentations II

12

Fri Dec 14 Project report

due at 5:00pm
template
*schedule is tentative and is subject to change.

Textbook
The required textbook for the course is
■ Ian Goodfellow, Yoshua Bengio, Aaron Courville. ​Deep Learning.

Other recommended supplemental textbooks on general machine learning:

■ Duda, R.O., Hart, P.E., and Stork, D.G. ​Pattern Classication​. Wiley-Interscience. 2nd Edition. 2001.
■ Theodoridis, S. and Koutroumbas, K. ​Pattern Recognition. Edition 4​. Academic Press, 2008.
■ Russell, S. and Norvig, N. ​Articial Intelligence: A Modern Approach​. Prentice Hall Series in Articial
Intelligence. 2003.
■ Bishop, C. M. ​Neural Networks for Pattern Recognition​. Oxford University Press. 1995.
■ Hastie, T., Tibshirani, R. and Friedman, J. ​The Elements of Statistical Learning​. Springer. 2001.
■ Koller, D. and Friedman, N. ​Probabilistic Graphical Models​. MIT Press. 2009.

Recommended online courses

■ http://cs231n.stanford.edu/​ ​CS231n: Convolutional Neural Networks for Visual Recognition
 ■ http://web.stanford.edu/class/cs224n/​ CS224n: Natural Language Processing with Deep Learning
■ http://rll.berkeley.edu/deeprlcourse/​ CS 294: Deep Reinforcement Learning
■ http://distill.pub/​ Very nice explanations of some DL concepts

Deliverables/Graded Work
There will be six homework assignments, each consisting of written and/or coding problems, and a final project. The
project will be done in teams of 3-4 students and will have several deliverables including a proposal, progress
update(s), final report and a final in-class presentation. The course grade consists of the following:
■ Math prerequisite quiz 5%
■ Homeworks, best 4 of 5 45%
■ Project (including all components) 45%
■ Class participation 5%

Software/Hardware
Programming assignments and projects will be developed in the Python programming language. We will also use the
Tensorflow deep learning library for some homeworks and for the project. Students are expected to use the ​Shared
Computing Cluster (SCC)​ ​and/or their own machines to complete work that does not require a GPU. For the projects,
we will provide GPU resources.

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: <​http://www.bu.edu/cs/resources/laboratories/undergraduate-lab/​>

Late Policy
Late work will incur the following penalties
■ Final project report and presentation: 20% off per day up to 2 days
■ Homework 20% off per day, up to 3 days

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​.