CS 536: Machine Learning II (Spring 2023)

Course Information

Instructor: Hao Wang
Email: hogue.wang@rutgers.edu
Office: CBIM 008

TA: Runhui Wang and Kai Mei
TA Email: runhui.wang@rutgers.edu and kai.mei@rutgers.edu
TA Office: CBIM and CoRE

Time: Friday, 12:10 pm-3:10 pm
Location: FBO-EHA

Office Hours: Monday, 3:00-4:00 pm or by appointment
Please use this Zoom link.

TA Office Hours: Tuesday, 10:00-11:00 am or by appointment (TA: Runhui Wang)
Thursday, 4:00-5:00 pm or by appointment (TA: Kai Mei)
Please use this Zoom link.

Mask Requirement in Class

Masks should conform to CDC guidelines and should completely cover the nose and mouth: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/about-face-coverings.html


  • TBD.

  • Course Descriptions

    In this course, we will cover the following topics in machine learning:

  • Machine learning basics
  • Deep learning architectures:
  • Multi-Layer Perceptrons (MLP)
  • Activation Functions, Backpropagation, Vanishing/Exploding Gradinent
  • Convolutional Neural Networks (CNN)
  • Modern CNN
  • (Modern) Recurrent Neural Networks (RNN)
  • Attention Operations, Transformer, BERT
  • Advanced topics on deep learning:
  • Optimization for Deep Learning
  • Deep Generative Models including VAE and GAN

  • Prerequisites

  • CS 520 (Introduction to AI) or CS 530 (Principles of AI).
  • Students must be familiar with python programming. The mini-project (and most probably the course project) will be based on python. You could use your own computer, Rutgers iLab, or Google Colab to run the code.

  • Expected Work

  • 40%: 2-3 Homework Assignments
  • 60%: Course Project (Proposal, Presentation, and Report)

  • Infrastructure Requirements

  • Please visit the Rutgers Student Tech Guide page for resources available to all students. If you do not have the appropriate technology for financial reasons, please email Dean of Students deanofstudents@echo.rutgers.edu for assistance. If you are facing other financial hardships, please visit the Office of Financial Aid at https://financialaid.rutgers.edu/.

  • Computing Resources

  • Rutgers iLab Servers: https://report.cs.rutgers.edu/nagiosnotes/iLab-machines.html
  • Google Colab: https://colab.research.google.com

  • Textbooks and Materials

    There is no textbook for this course. However, the following books can be useful though as references on relevant topics:

  • Deep Learning (DL), Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron, MIT Press, 2016, ISBN: 9780262035613
  • Dive into Deep Learning (https://d2l.ai/)
  • Pattern Recognition and Machine Learning (PRML), Christopher C. Bishop, Springer, 2006, ISBN: 9780387310732
  • Natural Language Processing with Distributed Representations (NLP), Kyunghyun Cho, https://arxiv.org/abs/1511.07916

  • You may also find this tuturial Deep Learning with PyTorch: A 60 Minute Blitz helpful.

    For the topic of reinforcement learning, some useful materials are:

  • Reinforcement Learning (2nd)
  • Lecture by David Silver

  • Tips and More Details on Final Projects

    Below are some tips and details.

    Team Forming: Students can form a team of at most six (recommended team size is 3-5).

    Tips for Course Project Presentation: Here are some tips that can make the project presentations smoother and more effective:

  • If the project is based on published papers, at the start of presentation you could introduce some background and details on the paper. To do this more effectively, you could look for demo videos on the Internet. Showing the demo as part of the presentation can tremendously help others understand the paper. You could also look for talks/slides on the paper available online (from YouTube or the authors’ websites). You can re-use some of the content if you feel it is helpful.
  • Pause for questions. At some points of your presentation, you could pause and ask if there are any questions.

  • Tentative Schedule

    Note that this is a tentative syllabus to give you an idea of what topics this course will cover. This syllabus is subject to change as the course progresses.

    Week Date Topic Assignment
    Machine Learning Basics
    1 Jan 20 Course Introduction and Machine Learning Basics (1)
    2 Jan 27 Machine Learning Basics (2) HW1 Release
    3 Feb 3 Linear Models Reading: Warmup on PyTorch
    Deep Learning Architectures
    4 Feb 10 Multi-Layer Perceptrons (MLP)
    5 Feb 17 Convolutional Neural Networks (CNN)
    6 Feb 24 Modern CNN HW2 Release
    7 Mar 3 (Modern) Recurrent Neural Networks (RNN)
    8 Mar 10 Attention Operations and Transformer Reading: Ch 10 & 11 of D2L
    9 Mar 17 Spring Recess
    10 Mar 24 Transformer, BERT, and GPT Optional HW3 Release
    Advanced Topics on Deep Learning
    11 Mar 31 Optimization for Deep Learning and Deep Generative Models - VAE I
    12 Apr 7 Deep Generative Models - VAE II and GAN
    Mini Conference
    13 Apr 14 Final Project Presentation (Mini Conference) I
    14 Apr 21 Final Project Presentation (Mini Conference) II
    15 Apr 28 Final Project Presentation (Mini Conference) III

    Rutgers CS Diversity and Inclusion Statement

    Rutgers Computer Science Department is committed to creating a consciously anti-racist, inclusive community that welcomes diversity in various dimensions (e.g., race, national origin, gender, sexuality, disability status, class, or religious beliefs). We will not tolerate micro-aggressions and discrimination that creates a hostile atmosphere in the class and/or threatens the well-being of our students. We will continuously strive to create a safe learning environment that allows for the open exchange of ideas while also ensuring equitable opportunities and respect for all of us. Our goal is to maintain an environment where students, staff, and faculty can contribute without the fear of ridicule or intolerant or offensive language. If you witness or experience racism, discrimination micro-aggressions, or other offensive behavior, you are encouraged to bring it to the attention to the undergraduate program director, the graduate program director, or the department chair. You can also report it to the Bias Incident Reporting System.