CSE 30124 is test an elective course in the Computer Science and Engineering program at the University of Notre Dame. This course serves as an introduction and gateway to upper level machine learning and artificial intelligence courses. In this course students will learn the fundamentals of learning algorithms and the basics of common python libraries for these algorithms such as scikit-learn and pytorch.

Upon successful completion of this course, students will be able to:

  1. Evaluate which model is appropriate for a given problem.

  2. Utilize modern python libraries for ML and AI.

  3. Implement basic ML and AI models.

  4. Assess AI/ML implementations for common issues and biases.

Class Information

Lecture
M / W 3:30 PM - 4:45 PM
Location
DeBart 155
Zoom Meeting
686 290 5283
Mailing List (Class)
fa24-cse-30124-01-group@nd.edu
Mailing List (Staff)
fa24-cse-30124-01-staff-list@nd.edu
Slack
#cse-30124-fa24
GitHub
nd-cse-30124-fa24

Instructor

Instructor
Bill Theisen (wtheisen@nd.edu)
Office Hours
T/TR 10:00 AM - 3:00 PM, and by appointment
Office Location
356B Fitz

Teaching Assistants

Graduate Teaching Assistant
Doheon Han (dhan6@nd.edu)
Graduate Teaching Assistant
Yiyang (Ian) Li (yli62@nd.edu)
Undergraduate Teaching Assistant
Mayleen Liu (mliu5@nd.edu)
Undergraduate Teaching Assistant
Thomas Lohman (tlohman@nd.edu)
Undergraduate Teaching Assistant
Francisco Septien (fquintan@nd.edu)
Undergraduate Teaching Assistant
Renee Shi (rshi2@nd.edu)

Help Protocol

  1. Think
  2. Slack
  3. Think
  4. Email
  5. Think
  6. Office

Office Hours

Unit Date Topics Assignments
Welcome Wed 08/28 Syllabus, ML as Function Approximation Slides
Unit 01: History of Artificial Intelligence
Good Old Fashioned AI (GOFAI) Mon 09/02 Agents and A* Slides Homework 00
Wed 09/04 Adversarial Search Slides
Mon 09/09 Constraint Satisfaction Slides Quiz 01 Quiz 01 Solutions Homework 01
Wed 09/11 Formal Logic, Symbolic Reasoning, and Knowledge Representation Slides
Mon 09/16 Bayesian Statistics and Naive Bayes Slides Notebook Quiz 02 Quiz 02 Solutions Homework 02
Wed 09/18 Review Notebook Panopto
Mon 09/23 Perceptrons Slides Notebook Panopto Quiz 03 Quiz 03 Solutions Homework 03
Exam 01 Wed 09/25 Exam 01 Exam 01 Exam 01 Solutions
Unit 02: Machine Learning
Supervised Learning Mon 09/30 Linear Algebra Review and Introduction to Machine Learning Slides Panopto
Wed 10/02 Linear Regressions Slides Notebook Panopto
Mon 10/07 Support Vector Machines Slides Notebook Quiz 04 Quiz 04 Solutions Homework 04
Unsupervised Learning Wed 10/09 Clustering Slides Notebook Panopto
Mon 10/14 Dimensionality Reduction Slides Panopto Quiz 05 Quiz 05 Solutions Homework 05
Using Machine Learning Wed 10/16 Cancelled - Evaluating Machine Learning Models Slides
Fall Break
Using Machine Learning Mon 10/28 Machine Learning Practicum Slides Notebook
Wed 10/30 Review Slides Panopto
Midterm 02 Mon 11/04 Exam 02 Homework 06 Quiz 06 Quiz 06 Solutions Exam 02 Exam 02 Solutions
Unit 03: Deep Learning
Deep Learning Wed 11/06 Introduction to Deep Learning, MLPs and FFNs Slides
Mon 11/11 Gradient Descent and Optimization Slides Notebook Panopto
Wed 11/13 Backpropogation Slides Notebook Panopto
Mon 11/18 Sequence Networks Slides Notebook Panopto Quiz 07 Homework 0708
Wed 11/20 Review
Midterm 03 Mon 11/25 Exam 03 Exam 03 Quiz 08
Unit 04: Modern Applications of Artificial Intelligence
The Revolution Mon 12/02 Transformers
Computer Vision Wed 12/04 Computer Vision
Natural Language Processing (NLP) Mon 12/09 Modern Natural Language Processing Quiz 09 Homework 0910
Looking Foward Wed 12/11 Agents and Reinforcement Learning Quiz 10
Final Tue 12/17 Final Exam, 8:00 AM - 10:00 AM, Debart 155 (Normal Classroom) Final Exam

Coursework

Component Points
Homeworks Weekly Homework Assignments. (Drop 1) 10 × 10
Quizzes Weekly Quizzes. 10 × 5
Midterms Midterm Exams 3 × 30
Exams The Final Exam 1 × 60
Total 300

Grading

Grade Points Grade Points Grade Points
A 279-300 A- 270-278
B+ 260-269 B 250-259 B- 240-249
C+ 230-239 C 220-229 C- 210-219
D 195-209 F 0-194

Due Dates

All Homeworks are to be submited to your own private GitHub repository. Unless specified otherwise:

  • Homeworks are due at midnight on the Sunday of each week.

Policies

Participation

Students are expected to attend and contribute regularly in class. This means answering questions in class, participating in discussions, and helping other students.

Foreseeable absences should be discussed with the instructor ahead of time.

Community

Recalling one of the tenets of the Hacker Ethic:

Hackers should be judged by their hacking, not criteria such as degrees, age, race, sex, or position.

Students are expected to be respectful of their fellow classmates and the instructional staff.

Students with Disabilities

Any student who has a documented disability and is registered with Disability Services should speak with the professor as soon as possible regarding accommodations. Students who are not registered should contact the Office of Disabilities.

Academic Honesty

Any academic misconduct in this course is considered a serious offense, and the strongest possible academic penalties will be pursued for such behavior. Students may discuss high-level ideas with other students, but at the time of implementation (i.e. programming), each person must do his/her own work. Use of the Internet as a reference is allowed but directly copying code or other information is cheating. It is cheating to copy, to allow another person to copy, all or part of an exam or a assignment, or to fake program output. It is also a violation of the Undergraduate Academic Code of Honor to observe and then fail to report academic dishonesty. You are responsible for the security and integrity of your own work.

Late Work

In the case of a serious illness or other excused absence, as defined by university policies, coursework submissions will be accepted late by the same number of days as the excused absence.

Otherwise, there is an automatic 25% late penalty for assignments turned in 12 hours pass the specified deadline.

Classroom Recording

This course will be recorded using Zoom and Panopto. This system allows us to automatically record and distribute lectures to you in a secure environment. You can watch these recordings on your computer, tablet, or smartphone. In the course in Sakai, look for the "Panopto" tool on the left hand side of the course.

Because we will be recording in the classroom, your questions and comments may be recorded. Recordings typically only capture the front of the classroom, but if you have any concerns about your voice or image being recorded please speak to me to discuss your concerns. Except for faculty and staff who require access, no content will be shared with individuals outside of your course without your permission.

These recordings are jointly copyrighted by the University of Notre Dame and your instructor. Posting them to other websites (including YouTube, Facebook, SnapChat, etc.) or elsewhere without express, written permission may result in disciplinary action and possible civil prosecution.

CSE Guide to the Honor Code

For the assignments in this class, you are allowed to consult printed and online resources and to discuss the class material with other students. You may also consult AI Tools such as CoPilot or ChatGPT for help explaining concepts, debugging problems, or as a reference. Viewing or consulting solutions, such as those from other students, previous semesters, or generated by AI Tools is never allowed.

Likewise, you may copy small and trivial snippets from books, online sources, and AI Tools as long as you cite them properly. However, you may not copy solutions or significant portions of code from other students or online sources, nor may you generate solutions via AI Tools.

Finally, when preparing for exams in this class, you may not access exams from previous semesters, nor may you look at or copy solutions from other current or former students.

Resources Solutions
Consulting Allowed Not Allowed
Copying Cite Not Allowed

See the CSE Guide to the Honor Code for definitions of the above terms and specific examples of what is allowed and not allowed when consulting resources.

If you are unclear about whether certain forms of consultation or common work are acceptable or what the standards for citation are, you responsible for consulting your instructor.

If an instructor sees behavior that is, in his judgement, academically dishonest, he is required to file either an Honor Code Violation Report or a formal report to the College of Engineering Honesty Committee.

Health and Safety Protocols

In this class, as elsewhere on campus, students must comply with all University health and safety protocols, including:

  • Students who are not fully vaccinated must wear masks inside campus buildings.
  • Students are expected to carry a mask at all times.
  • Students are to be considerate and understanding of the medical needs and personal beliefs of others.

We are part of a community of learning in which compassionate care for one another is part of our spiritual and social charter. Consequently, compliance with these protocols is an expectation for everyone enrolled in this course. If a student refuses to comply with the University’s health and safety protocols, the student must leave the classroom and will earn an unexcused absence for the class period and any associated assignments/assessments for the day. Persistent deviation from expected health and safety guidelines may be considered a violation of the University’s "Standards of Conduct,” as articulated in du Lac: A Guide for Student Life, and will be referred accordingly.

One of the benefits of ML/AI being extremely popular is there are many online resources available for learning it. The best teachers of the topics release much of their material avaible for free online. If something in class seemed unclear, you're encouraged to seek out an explanation that makes the most sense to you! If you find one that you really like, please share it with the rest of the class.