Fall 2024: Machine Learning [ CS688 ]

  • Class Webpage

    • General Description and Preliminary List of Topics
    Machine learning studies computer algorithms for learning to do things. For example, we might be interested in learning to complete a task, or to make accurate predictions, or to navigate in an unexplored environment. The learning that is being done is always based on some sort of observations or data, such as examples (the most common case in this course), direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past. The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance.

    The machine learning paradigm can be viewed as “programming by example.” Often we have a specific task in mind, such as recognizing handwritten digits on an envelope to perform automated mail dispatching. But rather than program the computer with rules to solve the task directly, in machine learning, we seek methods by which the computer will come up with its own program based on examples that we provide.

    The course covers key algorithms and theory at the core of machine learning. Particular emphasis will be given to the statistical learning aspects of the field. Topics include: decision theory, Bayesian theory, curse of dimensionality, dimensionality reduction techniques, classification, clustering, neural networks, kernel methods, mixture models and EM, deep learning. If time permits, additional topics may be covered.

    Course Format
    Lectures by the instructor. Besides material from the textbook, topics not discussed in the book may also be covered. Research papers and handouts of material not covered in the book will be made available.

    Classroom Specifics
    I expect students to attend the class. I supplement the textbook with extensive discussions and material. Students' active participation is very important to succed in this course.

    Communication
    We use Piazza for communication, and to enable questions and students' discussions. Class material, handouts, and grades are posted on Blackboard. We use GradeScope for submission of homework assignments and project's material.

    Grading Policy
    Grading will be based on quizzes, an exam, and a project. Homework assignments will be given and discussed in class, but not graded. In order to learn the material and to do well on quizzes and the exam, students are required to work on the assignments and turn in the solutions by the deadline. Students will be asked to present and discuss their solutions in class. No make-up quizzes or make-up exams will be offered. The lowest grade on the quizzes will be dropped. Graded work must be done on an individual basis, unless otherwise stated by the instructor. No extra work will be offered at the end-of-semester for extra credit. If you're not happy with the trajectory of your grade, change something and change it fast! Any deviation from this policy will be considered a violation of the GMU Honor Code.

    Grade breakdown:

    Quizzes: 30%
    Participation (includes in-class discussion of HWs): 5%
    Midterm: 30%
    Project: 35%

    Honor Code
    This class enforces the GMU Honor Code and the more specific honor code policy special to the Department of Computer Science. You will be expected to adhere to this code and policy.

    Disabilities
    If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and talk to me about accommodations.