CSE 30124 is 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
Fitz 356
Slack
#cse-30124-sp25
GitHub
nd-cse-30124-sp25

Instructor

Instructor
Bill Theisen (wtheisen@nd.edu)
Please use Dr./Professor Bill or Dr./Professor Theisen
Office Hours
Thursdays 1:00 PM - 4:00 PM, and by appointment
Office Location
356B Fitz

Teaching Assistants

Graduate Teaching Assistant
Tyler Berg (tberg3@nd.edu)
Undergraduate Teaching Assistant
Thomas Lohman (tlohman@nd.edu)
Undergraduate Teaching Assistant
Sophia Noonan (snoonan2@nd.edu)
Undergraduate Teaching Assistant
Francisco Septien (fseptien@nd.edu)
Graduate Teaching Assistant
Sam Webster (swebster@nd.edu)
Undergraduate Teaching Assistant
Olivia Zino (ozino@nd.edu)
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
10:00 AM - 11:00 AM
Tyler Berg
10:30 AM - 12:30 PM
CSE Commons
Francisco Septien
10:00 AM - 12:00 PM
Zoom
11:00 AM - 12:00 PM
12:00 PM - 1:00 PM
1:00 PM - 2:00 PM
Sam Webster
1:00 PM - 3:00 PM
CSE Commons
Bill Theisen
1:00 PM - 4:00 PM
356B Fitz
Thomas Lohman
1:00 PM - 2:30 PM
CSE Commons
2:00 PM - 3:00 PM
Olivia Zino
2:00 PM - 4:00 PM
Cushing 217
Olivia Zino
2:00 PM - 4:00 PM
CSE Commons
3:00 PM - 4:00 PM
Sophia Noonan
3:30 PM - 5:30 PM
CSE Commons
Francisco Septien
3:00 PM - 5:00 PM
Inno Lounge
4:00 PM - 5:00 PM
Sophia Noonan
4:00 PM - 5:30 PM
CSE Commons
5:00 PM - 6:00 PM
Thomas Lohman
5:00 PM - 6:45 PM
CSE Commons
6:00 PM - 7:00 PM
7:00 PM - 8:00 PM
8:00 PM - 9:00 PM
Unit Date Topics Assignments
Welcome Mon 01/13 Syllabus, History of AI Slides Panopto
Unit 01: Good Old Fashioned AI (GOFAI)
Search and Representations Wed 01/15 Search and Agents Slides
Mon 01/20 Cancelled in Observance of the National Championship Game (MLK Day)
Wed 01/22 A* Search Course Primer
Mon 01/27 Knowledge Representation and Feature Engineering
Wed 01/29 Constraint Satisfaction Problems
Mon 02/03 Adversarial Search Homework 01
Probabilistic Models* Wed 02/05 Decision Trees
Mon 02/10 Hidden Markov Models
Wed 02/12 Bayesian Reasoning
Exam 01 Mon 02/17 Review
Wed 02/19 Exam 01 Exam 01 Practice Packet Exam 01
Unit 02: Machine Learning
Supervised Learning Mon 02/24 Linear Regression Homework 02
Wed 02/26 Perceptrons
Mon 03/03 Logistic Regression
Wed 03/05 SVMs and KNNs
Spring Break
Unsupervised Learning Mon 03/17 Clustering
Wed 03/19 Dimensionality Reduction
Using Machine Learning Mon 03/24 Practicum - Evaluation and Validation of ML Systems Homework 03
Midterm 02 Wed 03/26 Review
Mon 03/31 Exam 02 Exam 02 Practice Packet Exam 02
Unit 03: Deep Learning
Foundations of Deep Learning Wed 04/02 Multi-Layer Perceptrons (FFNs)
Mon 04/07 Gradient Descent and Backpropagation
Wed 04/09 Convolutional Neural Networks
Mon 04/14 Recurrent Neural Networks Homework 04
Modern Deep Learning Wed 04/16 Attention
Mon 04/21 Cancelled (Easter Monday)
Wed 04/23 Transformers and Agentic LLMs
Mon 04/28 Diffusion Models
Wed 04/30 Reinforcement Learning Final Practice Packet Homework 05 Bonus Homework CIF Bribe
Final ??? Final Exam Final Exam


Note: Pobabilistic models aren't really GOFAI but I didn't want the first midterm to be the Monday after JPW

Coursework

Component Points
Course Primer Course Primer 1 × 5
Homeworks Group Homework Assignments 5 × 20
Midterms Midterm Exams (5 points for turning in exam practice packet) 2 × 60
Exams Final Exam (5 points for turning in exam practice packet) 1 × 75
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 Monday of the due 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.

Submit any questions or suggestions to this anonymous google form: Questions and Suggestions


Note: This form is genuinely anonymous but anonymity is a priviledge. Please don't misuse it.

If you're interested in being a TA please apply via this google form: TA Applications


Note: Applications are due by the day of the second exam and will be evaluated shortly after.

Note: TAing for CSE 30124 is quite competitive and usually there are only 1 or 2 open slots a semester (if any), so it may be worth having a backup plan.

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. Below are links to some books and blogposts that I personally find very useful.

Books