CS 342 - Neural networks - Fall 2020

MWF 10am-11am

All class and office hours will be through Zoom.
For all Zoom links, see the Zoom tab in Canvas.

Instruction Team
If you’d like to meet outside of office hours, please email.

Name (email) Office Hours
Instructor Brady Zhou ( brady.zhou (at) utexas.edu) T 10am-11am
TA Jeffrey Zhang ( jozhang (at) cs.utexas.edu) F 11am-12pm
TA Jierui Lin ( jerrylin (at) cs.utexas.edu) T 5pm-6pm
TA Yue Zhao ( yzhao (at) cs.utexas.edu) Th 5pm-6pm

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 week, 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.
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.


Wed Aug 26 Introduction
Fri Aug 28 Coding - Estimating PI colab
Sun Aug 30 up to and including Background.Summary
Mon Aug 31 Quiz
Wed Sep 02 Coding - K-Nearest Neighbors colab
Fri Sep 04 Coding - Part II
Sun Sep 06 up to and including First example.Summary
Mon Sep 07 No class - Labor Day
Wed Sep 09 Coding - Linear Classifier colab
Fri Sep 11 Coding - Part II
Sun Sep 13 up to and including Deep networks.Summary
Mon Sep 14 Quiz
Wed Sep 16 Coding - MLP colab
Fri Sep 18 Coding - Part II
Sun Sep 20 Homework 1 due 11:59pm
Sun Sep 20 up to and including Convolutional Networks.Building efficient convolutional networks
Mon Sep 21 Quiz
Wed Sep 23 Coding - CNN colab
Fri Sep 25 Coding - Part II
Sun Sep 27 up to and including Making it work.Initialization in pytorch
Mon Sep 28 Quiz
Wed Sep 30 Coding - CNN Blocks colab
Fri Oct 02 Coding - Part II
Sun Oct 04 Homework 2 due 11:59pm
Sun Oct 04 up to and including Making it work.Open Problem: Pruning and compression
Mon Oct 05 Quiz
Wed Oct 07 Coding - Image Generation colab
Fri Oct 09 Coding - Part II
Sun Oct 11 up to and including Making it work.Summary, a practical guide to deep network optimization
Mon Oct 12 Quiz
Wed Oct 14 Coding - Binary Segmentation colab
Fri Oct 16 Coding - Part II
Sun Oct 18 Homework 3 due 11:59pm
Sun Oct 18 up to and including Computer Vision.Case Study: RetinaNet
Mon Oct 19 Quiz
Wed Oct 21 Coding - Keypoint Estimation colab
Fri Oct 23 Coding - Part II
Sun Oct 25 up to and including Computer Vision.Summary
Mon Oct 26 Quiz
Wed Oct 28 Coding - Keypoints Continued colab
Fri Oct 30 Coding - Part II
Sun Nov 01 Homework 4 due 11:59pm
Sun Nov 01 up to and including Sequence modeling.Summary
Mon Nov 02 Quiz
Wed Nov 04 Coding - Imitation Learning colab
Fri Nov 06 Coding - Part II
Sun Nov 08 up to and including Reinforcement learning.Gradient free optimization
Mon Nov 09 Quiz
Wed Nov 11 Coding - Imitation Learning Continued. colab
Fri Nov 13 Coding - Part II
Sun Nov 15 Homework 5 due 11:59pm
Sun Nov 15 up to and including Embedding learning.Summary
Mon Nov 16 Quiz
Wed Nov 18 Coding - Part I
Fri Nov 20 Coding - Part II
Sun Nov 22 up to and including Adversarial attacks.Summary
Mon Nov 23 Quiz
Wed Nov 25 No class - Thanksgiving
Fri Nov 27 No class - Thanksgiving
Sun Nov 29 No lectures
Mon Nov 30 Research presentation - Philipp
Wed Dec 02 Project presentations I
Fri Dec 04 Project presentations II
Sun Dec 06 Extra Credit Homework due 11:59pm
Sun Dec 06 Final project due 11:59pm
Sun Dec 06 up to and including Final words.Open Problem: Bias, fairness, and ethics in deep learning
Mon Dec 07 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


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 Content

Course creators: Philipp Krähenbühl and Chao-Yuan Wu

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:

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 11, 2020