CS 342 - Neural networks - Fall 2019

meets
MWF 10am-11am UTC 3.102

instructor Philipp Krähenbühl ( philkr (at) utexas.edu )
office hours M 11am-noon GDC 4.824
Please only visit during office hours or with appointment (or if I didn’t reply your email after the third try)

TA
Xingyi Zhou ( zhouxy (at) cs.utexas.edu )
office hours W 1:30PM-2:30PM GDC 1.302 (Basement) Desk 5
Brady Zhou ( bzhou (at) cs.utexas.edu )
office hours W 11:30AM-12:30PM GDC 1.302 (Basement) Desk 1
Ishan Nigam ( ishann (at) cs.utexas.edu )
office hours M 11:30AM-12:30PM GDC 1.302 (Basement) Desk 1

This course covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. We will cover both the theory of deep learning, as well as hands-on implementation sessions in pytorch. We will also cover a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning. In the homework assignments, we will develop a vision system for a racing simulator, SuperTuxKart, from scratch.

Prerequisites: Python, basic ML background

Textbooks: None

Class overview

We will use Piazza for questions and canvas homework. CS 342 follows the flipped classroom model, and delivers all course material online. The schedule below shows the due dates for all modules. Open problems are optional.

Each weak, we start with a quiz on the course material Monday, and then work through the results of the quiz and any questions in class. Wednesday and Friday, we work on a medium sized coding project related to the course material. The TAs and I will do the coding, but you’ll tell us what do code up. We’ll split the class into even groups, and each group will solve the problem individually. We’ll train any larger networks over night following the class, and then compare solutions.

Schedule

DateWhatMaterial
Wed Aug 28 Introduction
Fri Aug 30 Coding - Estimating PI ipynb html team 1 team 2 team 3
Sun Sep 01 up to and including Background.Summary
Mon Sep 02 No class - Labor day
Wed Sep 04 Coding - Part I ipynb html team 1 team 3 team 4
Fri Sep 06 Coding - Part II team 1 team 2 team 3 team 4
Sun Sep 08 up to and including First example.Summary
Mon Sep 09 Quiz
Wed Sep 11 Coding - Part I ipynb html team 1 team 2 team 3
Fri Sep 13 Coding - Part II team 1 team 2 team 3 team 4
Sun Sep 15 up to and including Deep networks.Summary
Mon Sep 16 Quiz
Wed Sep 18 Coding - Part I ipynb html team4
Fri Sep 20 Coding - Part II team 1 team 2 team 3 team 4
Fri Sep 20 Homework 1 due 11:59pm
Sun Sep 22 up to and including Convolutional Networks.Building efficient convolutional networks
Mon Sep 23 Quiz
Wed Sep 25 Coding - Part I ipynb html Phlipp
Fri Sep 27 Coding - Part II team 1 team 2 team 3 team 4
Sun Sep 29 up to and including Making it work.Initialization in pytorch
Mon Sep 30 Quiz
Wed Oct 02 Coding - Part I ipynb html team4
Fri Oct 04 Coding - Part II team 1 team 2 team 3 team 4
Fri Oct 04 Homework 2 due 11:59pm
Sun Oct 06 up to and including Making it work.Open Problem: Pruning and compression
Mon Oct 07 Quiz
Wed Oct 09 Coding - Part I ipynb html
Fri Oct 11 Coding - Part II team 1 team 2 team 3 team 4
Sun Oct 13 up to and including Making it work.Summary, a practical guide to deep network optimization
Mon Oct 14 Quiz
Wed Oct 16 Coding - Part I ipynb html
Fri Oct 18 Coding - Part II final ipynb final html
Fri Oct 18 Homework 3 due 11:59pm
Sun Oct 20 up to and including Computer Vision.Case Study: RetinaNet
Mon Oct 21 Quiz
Wed Oct 23 Coding - Part I ipynb html
Fri Oct 25 Coding - Part II
Sun Oct 27 up to and including Computer Vision.Summary
Mon Oct 28 Quiz
Wed Oct 30 Coding - Part I ipynb html
Fri Nov 01 Coding - Part II
Fri Nov 01 Homework 4 due 11:59pm
Sun Nov 03 up to and including Sequence modeling.Summary
Mon Nov 04 Quiz
Wed Nov 06 Coding - Part I ipynb html
Fri Nov 08 Coding - Part II ipynb
Fri Nov 08 Homework 5 due 11:59pm
Sun Nov 10 up to and including Reinforcement learning.Gradient free optimization
Mon Nov 11 Quiz
Wed Nov 13 Coding - Part I ipynb html
Fri Nov 15 Coding - Part II
Fri Nov 15 Homework 6 due 11:59pm
Sun Nov 17 up to and including Embedding learning.Summary
Mon Nov 18 Quiz
Wed Nov 20 Coding - Part I ipynb html
Fri Nov 22 Coding - Part II
Sun Nov 24 up to and including Adversarial attacks.Summary
Mon Nov 25 Quiz
Wed Nov 27 No class - Thanksgiving
Fri Nov 29 No class - Thanksgiving
Sun Dec 01 No lectures
Mon Dec 02 Research presentation - Philipp
Wed Dec 04 Project presentations I
Fri Dec 06 Project presentations II
Fri Dec 06 Final project due 11:59pm
Sun Dec 08 up to and including Final words.Open Problem: Bias, fairness, and ethics in deep learning
Mon Dec 09 Final project results

Assignments and final project

All homework are due at 11:59pm, but you’ll have one additional hour to upload your assignment to canvas (in case your internet connection is really slow). Please do not email me for further extensions.

Collaboration / Academic Honesty Policy

Collaboration is not allowed for homework or Quizzes. Every homework and quiz needs to be solved individually. We will check for duplicates. For the final project groups of up to 3 students are allows. Different groups are allowed to discuss ideas and share data, but no code.

The online course format allows for multiple methods of identity verification, collusion, collaboration and plagiarism monitoring and detection. A violation of the course policy may include (but is not limited to) the following:

The University of Texas at Austin Academic Integrity principles call for students to avoid engaging in any form of academic dishonesty on behalf of yourself or another student. Grade-related penalties are routinely assessed (“F” in the course is not uncommon), but students can also be suspended or even permanently expelled from the University for scholastic dishonesty.

If you have any questions about what constitutes academic dishonesty, please refer to the Dean of Students website or contact the instructor for this course.

You must agree to abide by the Honor Code of the University of Texas. You will not work with or collaborate with others in any way while completing any of the graded course assignments.

Late Policy

Quizzes: N/A

Homework:

Final Project:

You have a total of 3 slip-days for your homework. There are no slip-days for the final project or quizzes. A homework that is 3 days late will be graded at -100% irrespective of slip-days, as we will release the solution on day 3.

Assignments, Assessment, Evaluation

Documented Disability Statement

The University of Texas at Austin guarantees that students with disabilities have access to appropriate accommodations. You may request an accommodation letter from the Division of Diversity and Community Engagement, Services for Students with Disabilities https://diversity.utexas.edu/disability/.

If you have approved accommodations for the course, please contact us to arrange them. Please do this as soon as possible, so that you can have the benefit of the accommodations throughout the duration of the course.

Course Etiquette

We expect that you will treat online discussions as though you are having a civil, respectful discussion with your fellow classmates in the same classroom. Please refrain from using profanity or any euphemisms for profanity. Please do not bait other commenters or personally attack them. Please do not use sarcasm in a way that can be misinterpreted negatively. And please do not make the same point over and over again. In short, please just respect the right of your colleagues to ask questions and discuss their opinions about the subject matter of our course on the discussion board. Violators of these discussion rules will simply be shut out from all class communications—email, Piazza, and office hours.

Behavior Concerns Advice Line

If you are worried about someone who is acting differently, you may use the Behavior Concerns Advice Line to discuss by phone your concerns about another individuals behavior. This service is provided through a partnership among the Office of the Dean of Students, the Counseling and Mental Health Center (CMHC), the Employee Assistance Program (EAP), and The University of Texas Police Department (UTPD). Call 512-232-5050 or visit https://besafe.utexas.edu/behavior-concerns-advice-line

Course Conent

Course creators: Philipp Krähenbühl and Chao-Yuan Wu

Instructor: Philipp Krähenbühl

TA Support: Dian Chen and Thanh An Nguyen

Using this material in your own course

Feel free to build on this course material. We only ask that you attribute us appropriately. For course material:

For homework:

License:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License

The code for all homework assignments (and coding examples in class) is released under the MIT license.

Getting access to this repo

If you’re teaching a similar class and would like to gain raw access to the course material, shoot me an email and I can add you. To make things as easy as possible use the subject Access to deep learning class repo and make sure to give me your github id. Please briefly describe why you’d need access including a link to the course you’re going to teach. I’d ask you to not share any solutions online and keep the attributions in place.

All material, except for the homework solutions can be directly accessed from this webpage.

This syllabus is subject to change. Last updated: November 25, 2019