CS 535: Machine Learning I (Fall 2022)

Course Information


Instructor: Hao Wang
Email: hogue.wang@rutgers.edu
Office: CBIM 008
TA: Haizhou Shi
TA Email: hs1107@rutgers.edu
TA Office: CBIM
Time: Monday, 10:20 am-1:20 pm
Location: PH 111
Office Hours: Wednesday, 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
Please use this Zoom link.

Mask Requirement in Class

In order to protect the health and well-being of all members of the University community, masks must be worn by all persons on campus when in the presence of others (within six feet) and in buildings in non-private enclosed settings (e.g., common workspaces, workstations, meeting rooms, classrooms, etc.). Masks must be worn during class meetings; any student not wearing a mask will be asked to leave.

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

Each day before you arrive on campus or leave your residence hall, you must complete the brief survey on the My Campus Pass symptom checker self-screening app.


Announcements

  • TBD.

    • Course Descriptions

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

  • Fundamentals:
  • Probability & random variables. Univariate & multivariate.
  • Maximum likelihood estimation. Risk & empirical risk minimization.
  • Decision theory & Information theory for Machine Learning.
  • Linear algebra for Machine Learning. Vector spaces. Matrix decomposition. Matrix calculus.
  • Optimization. First & second order methods. Stochastic gradient descent. Constrained optimization.
  • Linear discriminant analysis.
  • Logistic regression.
  • Linear regression.
  • Generalized linear models.
  • Exemplar-based methods. k nearest neighbors. Learning distance metrics. Kernel density estimation.
  • Kernel methods. Gaussian processes. Support vector machines and relevance vector machines.
  • Unsupervised learning and dimensionality reduction. Factor analysis and manifold learning.
  • Clustering. k means. Mixture models. Spectral clustering.


    • 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 only computer, Rutgers iLab, or Google Colab to run the code.

    • Expected Work

  • 20%: Mini-Project
  • 80%: 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:

  • Pattern Recognition and Machine Learning (PRML), Christopher C. Bishop, Springer, 2006, ISBN: 9780387310732 (link)
  • Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012/2022 (link)
  • Deep Learning (DL), Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron, MIT Press, 2016, ISBN: 9780262035613 (link)
  • Dive into Deep Learning (link)
  • Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow (link)
  • Natural Language Processing with Distributed Representations (NLP), Kyunghyun Cho, https://arxiv.org/abs/1511.07916

    • If you plan to work on the course project using deep learning, you may also find this tuturial Deep Learning with PyTorch: A 60 Minute Blitz helpful.

    Tips and More Details on Final Projects

    Below are some tips and details.

    Team Forming: Students can form a team of at most four.

    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 and Fundamentals
    1 Sep 12 Course Introduction and Machine Learning Basics Warmup Exercise Release
    2 Sep 19 Fundamentals I (Probability Univariate, Probability Multivariate, Statistics, Decision Theory)
    3 Sep 26 Fundamentals II (Information Theory, Linear Algebra & Matrix Calculus; Optimization)
    Linear Models and Their Bayesian Treatments
    4 Oct 3 Linear Models I (Linear Discriminant Analysis) Mini Project Release
    5 Oct 10 Linear Models II (Logistic Regression, Bayesian Logistic Regression)
    6 Oct 17 Linear Models III (Logistic Regression, Bayesian Logistic Regression)
    7 Oct 24 Linear Models IV (Linear Regression - Ridge, Lasso, Robust, Bayesian)
    8 Oct 31 Linear Models V (Linear Regression - Ridge, Lasso, Robust, Bayesian); Exponential Family and Conjugate Priors
    9 Nov 7 (Bi)Linear Models VI (Recommender Systems and Latent Factor Models)
    Kernel Methods
    10 Nov 14 Kernel Methods I
    11 Nov 21 Kernel Methods II
    12 Nov 28 Support Vector Machines (SVM)
    Mini Conference
    13 Dec 5 Final Project Presentation (Mini Conference) I
    14 Dec 12 Final Project Presentation (Mini Conference) II

    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.