CS 342 - Neural networks - Fall 2021

meets TTH 9:30am-11am in GDC 2.216 (zoom mirror; see canvas)

Class will be in-person with a live Zoom mirror. Office hours will be exclusively 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 Philipp Krähenbühl ( philkr (at) utexas.edu) Th 11am-12pm
TA Jeffrey Zhang ( jozhang (at) utexas.edu) T 12pm-1pm
TA Tianjian Meng ( mengtianjian (at) utexas.edu) W 4pm-5pm
TA Jierui Lin ( jerrylin (at) utexas.edu) Th 4pm-5pm
TA Tianwei Yin ( yintianwei (at) utexas.edu) T 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 Tuesday, and then work through the results of the quiz and any questions in class. In the second half of class, we start 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.


The class will be in-person, until disaster strikes. However, there is a live zoom link for anybody that is uncomfortable with an in-person setting. We wont record the zoom sessions.

Either in-person or zoom attendance is required. You may miss up to 3 classes without any excuse. We use an instapoll for attendance (please bring a phone or laptop).

Please join us over zoom if you have any health concerns, or may be sick.


Thu Aug 26 Introduction intro colab
Sun Aug 29 Up to and including Background.Summary
Tue Aug 31 Coding - K-Nearest Neighbors colab
Thu Sep 02 Coding - Part II
Sun Sep 05 Up to and including First example.Summary
Tue Sep 07 Coding - Linear Classifier colab
Thu Sep 09 Coding - Part II
Sun Sep 12 Up to and including Deep networks.Summary
Tue Sep 14 Coding - MLP colab
Thu Sep 16 Coding - Part II
Sun Sep 19 Homework 1 due 11:59pm
Sun Sep 19 Up to and including Convolutional Networks.Building efficient convolutional networks
Tue Sep 21 Coding - CNN colab
Thu Sep 23 Coding - Part II
Sun Sep 26 Up to and including Making it work.Initialization in pytorch
Tue Sep 28 Coding - CNN Blocks colab
Thu Sep 30 Coding - Part II
Sun Oct 03 Homework 2 due 11:59pm
Sun Oct 03 Up to and including Making it work.Open Problem: Pruning and compression
Tue Oct 05 Coding - Image Generation colab
Thu Oct 07 Coding - Part II
Sun Oct 10 Up to and including Making it work.Summary, a practical guide to deep network optimization
Tue Oct 12 Coding - Binary Segmentation colab / in class colab
Thu Oct 14 Coding - Part II
Sun Oct 17 Homework 3 due 11:59pm
Sun Oct 17 Up to and including Computer Vision.Case Study: RetinaNet
Tue Oct 19 Coding - Keypoint Estimation colab
Thu Oct 21 Coding - Part II
Sun Oct 24 Up to and including Computer Vision.Summary
Tue Oct 26 Coding - Keypoints Continued colab
Thu Oct 28 Coding - Part II
Sun Oct 31 Homework 4 due 11:59pm
Sun Oct 31 Up to and including Reinforcement learning.Gradient free optimization
Tue Nov 02 Coding - Imitation Learning colab
Thu Nov 04 Coding - Part II in-class colab
Sun Nov 07 Up to and including Sequence modeling.Summary
Tue Nov 09 Coding - Imitation Learning Continued. colab
Thu Nov 11 Coding - Part II
Sun Nov 14 Homework 5 due 11:59pm
Sun Nov 14 Up to and including Embedding learning.Summary
Tue Nov 16 Coding - Part I colab
Thu Nov 18 Coding - Part II
Sun Nov 21 Up to and including Adversarial attacks.Summary
Tue Nov 23 Philipp research presentation
Thu Nov 25 No class - Thanksgiving
Sun Nov 28 Up to and including Final words.Open Problem: Bias, fairness, and ethics in deep learning
Tue Nov 30 Final project presentations I
Thu Dec 02 Final project presentations II
Sun Dec 05 Makeup Homework due 11:59pm
Sun Dec 05 Final project due 11:59pm

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 4 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. Slip-days are applies greedily (automatically).

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

This syllabus is subject to change. Last updated: December 13, 2021