Course Syllabus

This course covers the principles and practices of machine learning algorithms, including the basics, mathematical definitions, and implementation with Python. It begins with an introduction to the foundations of machine learning algorithms, such as linear regression, logistic regression, and perceptron. Later sections focus on modern machine learning algorithms, particularly deep learning models such as CNNs, RNNs, and Transformers. Hands-on coding assignments using popular ML libraries like PyTorch are included to provide students with practical experience. Additionally, the course aims to inspire exploration of advanced algorithms in the field by introducing recent generative models. By the end of the course, students will be equipped to mathematically analyze ML algorithms and implement them using Python.

At the end of this course, students will be able to:

• Apply basic machine learning concepts, including various training paradigms, loss functions, optimization techniques, and hyperparameter tuning.
• Utilize fundamental deep learning concepts, such as backpropagation, dropout, and feed-forward neural networks.
• Understand documentation for a deep learning framework such as PyTorch and use it to implement simple deep learning models, such as feed-forward neural networks.
• Build a neural classifier from scratch, as well as its training and evaluation, for a given classification or regression problem using Python and PyTorch.

There is no official textbook for the class. Slides will be provided and reading materials for each topic will be assigned from the following references:

Recommended Texts

• Machine Learning: A Probabilistic Perspective, Kevin P Murphy, MIT Press (2012)
• Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT press (2016)

Instructor

Kenneth Marino
Office: MEB 2178
Email: kenneth.marino@utah.edu
Office Hours: M 4:30PM-5:30PM

TA

Shih-Chieh Dao
Office: MEB 3145
Email: shihchieh.dai@utah.edu
Office Hours: W 1:00PM-3:00PM

Preferred Contact Methods

For questions about homework, post first on piazza (or see if your question has been asked already) and/or come to office hours.

For questions about grades on homework/quizzes/exams, please TAs during office hours.

Your performance in this course will be evaluated by:

• 10% Quizzes
• 40% Homework Assignments
• 25% Midterm Exam
• 25% Final Exam

Formating

Each homework write-up must be neatly typeset as a PDF document. You can use LaTeX or any other system that produces typesetting of equal quality and legibility (especially for mathematical symbols and expressions). Please write your solutions as succinctly as possible while including all the necessary details. Ensure that the following appear at the top of the first page of the write-up: your name, your UID, and the ID’s of any students with whom you discussed the assignment. Submit your write-up as a single PDF file and corresponding code implementations (if any) on Canvas by 11:59 PM of the due date. It is your responsibility to ensure that the submission is successfully received by Canvas.

All assignments, unless otherwise announced, must be submitted to the designated area of
Canvas. Do not submit assignments via email.

Due Date Time

Assignments will be accepted up until 11:59 PM on the due date. The score for late homework is 0. Exceptions will be made in case of serious illness or bereavement. If a student has a planned absence from a class when an exam will be given, the student should make arrangements before the planned absence to take the exam early or take a makeup exam after returning to campus.

Grade Disputes

Feedback on graded material will be posted on Canvas in as timely a manner as possible. Once feedback for a graded assignment is posted, you will have 1 week from the posting date to dispute a grade. No re-grade requests will be honored after 1 week from posting feedback. Grade changes will not be made at the end of the semester.

Collaboration Policy

You are encouraged to discuss course materials and reading assignments, and homework assignments with each other in small groups (two to three people). You must list all discussants in your homework write-up. Discussion about homework assignments may include brainstorming and verbally discussing possible solution approaches, but must not go as far as one person telling others how to solve a problem. In addition, you must write-up your solutions by yourself, and you may not look at another student’s homework write-up/solutions (whether partial or complete).

AI Policy

AI use is allowed and encouraged as a study tool: generating practice problems, asking to explain concepts. However, it is not allowed to be used to complete homework assignments. Any use of AI to generate homework solutions will results in a 0 for the assignment.

There is a total of 6 days of grace period available for late submissions across all assignments. You may use this grace period for any assignment. However, please note that the grace period is cumulative. Once the 6-day grace period has been fully used, any further late submissions will receive a score of 0, in accordance with the assignment policy.

Grades will be determined based on correctness and relevance to the assignments and questions. Pay close attention to the instructions and rubrics provided for each assignment/task.

Extra credit opportunities may be available throughout the course. These will be clearly communicated and typically involve additional research, attending relevant events, or completing supplementary assignments. Extra credit can contribute up to 5% additional points to the final grade.

Grading scale

Disclaimer

Accommodations will be considered on an individual basis and may require documentation.

Please contact your instructor as soon as possible (preferably shortly before the semester begins) to request accommodations of any kind.

Content Warnings

Please be aware that some materials and discussions within this course may contain challenging content. Your instructor may choose to notify students of potentially difficult content (e.g. explicit language, graphic images, violent themes, etc.) throughout the course.

If there are specific subjects that you need advanced notice for, please contact your instructor at the beginning of the semester.

Extreme personal circumstances

Please contact your instructor as soon as possible if an extreme personal circumstance
(hospitalization, death of a close relative, natural disaster, etc.) is interfering with your ability to
complete your work.

Religious Practice

To request an accommodation for religious practices, contact your instructor at the beginning of the semester.

Active Duty Military

If you are a student on active duty with the military and experience issues that prevent you from participating in the course because of deployment or service responsibilities, contact your instructor as soon as possible to discuss appropriate accommodations.

Disability Access

All written information in this course can be made available in an alternative format with prior notification to the Center for Disability Services (CDS). CDS will work with you and the instructor to make arrangements for accommodations. Prior notice is appreciated. To read the full accommodations policy for the University of Utah, please see Section Q of the Instruction & Evaluation regulations.

If you will need accommodations in this class, contact:

Center for Disability Services
  801-581-5020
  disability.utah.edu
  162 Union Building
    200 S. Central Campus Dr.
     Salt Lake City, UT 84112

This syllabus is not a contract. It is meant to serve as an outline and guide for your course. Please note that your instructor may modify it to accommodate the needs of your class.

You will be notified of any changes to the Syllabus.