Introduction to Artificial Intelligence
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:
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Evaluate which model is appropriate for a given problem.
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Utilize modern python libraries for ML and AI.
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Implement basic ML and AI models.
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Assess AI/ML implementations for common issues and biases.
Logistics
Class Information
- Lecture
- M / W 3:30 PM - 4:45 PM
- Location
- 126 Debart
- Slack
- #cse-30124-fa25
- GitHub
- nd-cse-30124-fa25
Instructor
- Instructor
- Bill Theisen (wtheisen@nd.edu)
- Please use Dr./Professor Bill or
Dr./Professor Theisen - Office Hours
- Thursdays 1:00 PM - 5:00 PM, and by appointment
- Office Location
- 356B Fitz
Teaching Assistants
- Graduate Teaching Assistant
- Cesar Cervesa (ccerves@nd.edu)
- Graduate Teaching Assistant
- Thomas Lohman (tlohman@nd.edu)
- Undergraduate Teaching Assistant
- Jack Mangione (jmangion@nd.edu)
- Undergraduate Teaching Assistant
- Sophia Noonan (snoonan2@nd.edu)
- Undergraduate Teaching Assistant
- Olivia Zino (ozino@nd.edu)
Office Hours
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | |
|---|---|---|---|---|---|---|---|
| 10:00 AM - 11:00 AM |
Bill Theisen 10:00 AM - 12:00 PM 356B Fitz |
Jack Mangione 10:00 AM - 12:00 PM Zoom |
Bill Theisen 10:00 AM - 12:00 PM 356B Fitz |
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| 11:00 AM - 12:00 PM | |||||||
| 12:00 PM - 1:00 PM |
Cesar Cervesa 12:30 PM - 2:30 PM Inno Lounge |
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| 1:00 PM - 2:00 PM |
Bill Theisen 1:00 PM - 5:00 PM 356B Fitz |
Thomas Lohman 1:00 PM - 2:30 PM CSE Commons |
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| 2:00 PM - 3:00 PM |
Olivia Zino 2:00 PM - 4:00 PM CSE Commons |
Olivia Zino 2:00 PM - 3:00 PM CSE Commons |
Olivia Zino 2:00 PM - 3:00 PM CSE Commons |
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| 3:00 PM - 4:00 PM |
Lecture 3:30 PM - 4:45 PM 126 Debart |
Jack Mangione 3:00 PM - 5:00 PM Inno Lounge |
Lecture 3:30 PM - 4:45 PM 126 Debart |
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| 4:00 PM - 5:00 PM |
Sophia Noonan 4:00 PM - 5:30 PM CSE Commons |
Sophia Noonan 4:45 PM - 6:45 PM CSE Commons |
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| 5:00 PM - 6:00 PM |
Thomas Lohman 5:00 PM - 6:45 PM CSE Commons |
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| 6:00 PM - 7:00 PM | |||||||
| 7:00 PM - 8:00 PM | |||||||
| 8:00 PM - 9:00 PM |
Schedule
Click on the next to a topic for additional resources
| Unit | Date | Topics | Assignments |
|---|---|---|---|
| Welcome | Mon 08/25 | Syllabus, History of AI | |
| Unit 01: Good Old Fashioned AI (GOFAI) | |||
| Search and Representations | Wed 08/27 | Search | Reading 01 |
| Mon 09/01 | A* Search | ||
| Wed 09/03 | Constraint Satisfaction Problems | Reading 02 | |
| Mon 09/08 | Adversarial Search | ||
| Wed 09/10 | Knowledge Representation and Feature Engineering | Reading 03 | |
| Unit 02: Machine Learning | |||
| Direct Fit Models | Mon 09/15 | Decision Trees | Homework 01 |
| Wed 09/17 | Markov Models | Reading 04 | |
| Mon 09/22 | Bayesian Reasoning | ||
| Wed 09/24 | Linear Regression | Reading 05 | |
| Exam 01 | Mon 09/29 | Review | |
| Wed 10/01 | Exam 01 | Exam 01 Practice Packet Exam 01 | |
| Supervised Learning | Mon 10/06 | Perceptrons | Reading 06 |
| Wed 10/08 | Logistic Regression and Gradient Descent | ||
| Mon 10/13 | SVMs and KNNs | Homework 02 Reading 07 | |
| Unsupervised Learning | Wed 10/15 | Clustering | |
| Fall Break | |||
| Dimensionality Reduction | Mon 10/27 | Dimensionality Reduction | Reading 08 |
| Applied ML | Wed 10/29 | Practicum - Exploratory Data Analysis | |
| ML to DL | Mon 11/03 | Multi-Layer Perceptrons (FFNs) | Homework 03 Reading 09 |
| Wed 11/05 | Gradient Descent and Backpropagation | ||
| Midterm 02 | Mon 11/10 | Review | Reading 10 |
| Wed 11/12 | Exam 02 | Exam 02 Practice Packet Exam 02 | |
| Unit 03: Deep Learning | |||
| Foundations of Deep Learning | Mon 11/17 | Convolutional Neural Networks | Reading 11 |
| Wed 11/19 | Recurrent Neural Networks | Reading 12 | |
| Modern Deep Learning | Mon 11/24 | Self-Attention and Transformers | Homework 04 Reading 13 |
| Wed 11/26 | Cancelled (Thanksgiving Break) | ||
| Mon 12/01 | Reinforcement Learning | Reading 14 | |
| Wed 12/03 | LLMs and Agents (Guest Lecture) | ||
| Mon 12/08 | Diffusion Models | Reading 15 | |
| Wed 12/10 | Review | Final Practice Packet Homework 05 Bonus Homework CIF Bribe | |
| Final | Tue 12/16 | (TENTATIVELY) Final Exam (1:45 PM - 3:45 PM) | Final Exam |
There's loads of additional resources out there! If you find one that particuarly resonates with you, I'd appreciate it if you were willing to share it with the rest of the class. You'll even have the option to tag it with your name so future students can see who to thank!
Submit any additional resources to this google form: Additional Resources
Requirements
Coursework
| Component | Points |
|---|---|
| Reading Readings | 15 × 2 |
| Homework Group Homework Assignments | 5 × 20 |
| Midterm Midterm Exams (5 points for turning in exam practice packet) | 2 × 50 |
| Exam Final Exam (5 points for turning in exam practice packet) | 1 × 70 |
| 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
Readings are due at 3:00PM on the day of the due date.
Homeworks are due at midnight on the Monday of the due week.
Practice Packets and Exams
I do something a little different in this class than I've seen in other ones. Instead of giving you old versions of the exams with which to practice, I will release a "practice packet". If you turn in this practice packet on the day of the exam (either online or paper is fine) then you will receive points on the exam itself for actually studying. The practice packet is graded entirely on completion. There will be an entry in canvas worth 0 points for each practice packet, the points you receive for doing it will be reflected in the exam score itself.
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.
Resources
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.