CS 342 - Neural networks - Fall 2020
All class and office hours will be through Zoom.
For all Zoom links, see the Zoom tab in Canvas.
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
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.
|Fri||Aug 28||Coding - Estimating PI||colab|
|Sun||Aug 30||up to and including Background.Summary|
|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|
|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|
|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|
|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|
|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|
|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|
|Wed||Oct 21||Coding - Part I|
|Fri||Oct 23||Coding - Part II|
|Sun||Oct 25||up to and including Computer Vision.Summary|
|Wed||Oct 28||Coding - Part I|
|Fri||Oct 30||Coding - Part II|
|Sun||Nov 01||Homework 4 due 11:59pm|
|Sun||Nov 01||up to and including Sequence modeling.Summary|
|Wed||Nov 04||Coding - Part I|
|Fri||Nov 06||Coding - Part II|
|Sun||Nov 08||up to and including Reinforcement learning.Gradient free optimization|
|Wed||Nov 11||Coding - Part I|
|Fri||Nov 13||Coding - Part II|
|Sun||Nov 15||Homework 5 due 11:59pm|
|Sun||Nov 15||up to and including Embedding learning.Summary|
|Wed||Nov 18||Coding - Part I|
|Fri||Nov 20||Coding - Part II|
|Sun||Nov 22||up to and including Adversarial attacks.Summary|
|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
- homework 1 (10%)
- homework 2 (10%)
- homework 3 (10%)
- homework 4 (10%)
- homework 5 (10%)
- homework 6 (5% extra credit)
- final project (30%)
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:
- Providing your UT EID to any other person
- Collaborating or sharing information with another person regarding the material on any quiz, assessment or assignment, before, during and/or after any quiz, assessment or assignment
- Recording any quiz, assessment or assignment material in any format
- Failing to properly cite language, ideas, data, or arguments that are not originally yours
- The public (such that it can be viewed by more than one person) posting of any form of a test bank or group of questions from any assignment
- Consulting forbidden materials or sources of information
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.
- 1 day: -25%
- 2 days: -50%
- 3 days: -100%
- 1 day: - 100%
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
- Quizzes (10%): Quizzes (linear, may miss 2).
- Coding (10%): Attendance and involvement (may miss 2).
- Homework (50%): There are six homework assignments in this class (10% per assignment).
- Final Project (30%): There is one final project in this class.
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.
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 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:
- If you use an entire slide deck, simply leave the attribution on the first slide
- If you use a single slide, put an attribution somewhere on the slide
- Put the original license and attribution somewhere in the starter code.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International 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: October 13, 2020