CSE 40171 is an upper-level Computer Science and Engineering course at the University of Notre Dame that introduces students to the field of artificial intelligence. Once a topic of merely historical interest, artificial intelligence has re-emerged as one of the most vibrant subfields of computer science. Applications ranging from video games to autonomous vehicles now rely on breakthroughs related to data-driven learning algorithms and massively parallel hardware. And in light of these successes, one might think that the replication of general intelligence in a computer is just around the corner. However, the reality of the situation is quite the opposite: a comprehensive understanding of how the brain works has yet to emerge. While this divide is a significant stumbling block to progress, it also presents an intriguing opportunity to reverse engineer aspects of intelligence to forward engineer artificial solutions to real problems. A grasp of the limits and the potentials of various approaches to do this is essential. In this vein, this course will be a tour of the classic ideas and state-of-the-art methods in AI, both within the context of our current understanding of how the brain processes information. Throughout the semester, we will develop a framework for artificial intelligence around artificial neural networks that incorporates a model search framework, assessments of anatomical and functional fidelity with the brain, and a Bayesian cognitive read-out layer. An emphasis is placed on application domains where AI has achieved the most success: Games, Robotics, Computer Vision and Natural Language Processing.

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

  1. Understand the philosophical underpinnings of the field and motivations for pursuing the replication of certain competencies of the brain.

  2. Relate real-life problems to perceptual and cognitive models that are able to solve aspects of them in an efficient manner.

  3. Deploy general search algorithms that can be applied to a wide variety of tasks.

  4. Formulate decision making processes that can be used for planning and classification purposes.

  5. Build intelligent agents that perform simple tasks in an autonomous fashion.

  6. Learn task-specific models from large collections of labeled training data samples using algorithms that are optimized using numeric solvers.

  7. Utilize the Pytorch framework for building solutions to problems related to games, computer vision, natural language processing, and other general data science applications.

  8. Identify problems that are solvable with today's AI algorithms and others that require novel solutions.

  9. Grasp the aspects of artificial intelligence where neuroscience and computer science come together to form the basis of a new class of learning algorithms.

Class Information

M/W/F 3:30 PM - 4:20 PM
125 DeBartolo Hall


Walter Scheirer (walter.scheirer@nd.edu)
Office Hours
M/W 1:00 PM - 3:15 PM, and by appointment
Office Location
321C Stinson-Remick Hall

Help Protocol

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

Teaching Assistants

Graduate Teaching Assistant
Sophia Abraham (sabraha2@nd.edu)
Office Hours
FR 9:00 - 11:00 AM
Office Location
Hesburgh Library Research Commons
Teaching Assistant
Fiona McCarter (fmccarte@nd.edu)
Office Hours
MO 12:00 - 1:00 PM and by appointment
Office Location
Duncan Innovation Lounge
Teaching Assistant
Mike Eisemann (meiseman@nd.edu)
Unit Date Topics Assignment
Course Introduction 08/28 Introduction, Syllabus, History of AI Slides
08/30 Philosophical Perspectives Slides R&N Chpt. 1
Introduction to Biological Intelligence
The Brain 09/2 Perception Slides D. Marr on AI
09/4 Cognition Slides To Grow a Mind
09/6 Neural Computation Slides The Connectome
Machine Learning Model of Intelligence
Artificial Neural Networks 09/9 Structure of Neural Networks Slides R&N 18.7.0-18.7.3; Homework 01
09/11 Gradient-based Optimization Slides R&N 18.7.4-18.7.5
09/13 Artificial Neural Network Architectures Slides NN Zoo
Search Problems in AI
Uninformed Search 09/16 Search Spaces Slides R&N 3.1-3.2
09/18 Film Screening: AlphaGo Film Response
09/20 Film Screening: AlphaGo
09/23 Search Trees Slides R&N 3.3-3.4; Homework 02
Informed Search 09/25 A* Search Slides R&N 3.5
09/27 Search Heuristics Slides R&N 3.6
Constraint Satisfaction Problems 09/30 Defining CSPs Slides R&N 6.1
10/2 Inference and Backtracking Search Slides R&N 6.2-6.3; Homework 03
10/4 Local Search and Problem Structure Slides R&N 4.1, 6.4-6.5
Adversarial Search 10/7 Games, Optimality, and Minimax Slides R&N 5.1-5.2
10/9 Game Trees: Alpha-Beta Pruning; Imperfect Decisions Slides R&N 5.3-5.4
10/11 Game Trees: Expectimax; Partial Observability Slides R&N 5.5-5.8; Homework 04
Neural Network Model Search 10/14 High-Throughput Screening Slides Pinto et al. 2009
10/16 Hyperparameter Optimization Strategies Slides Bergstra et al. 2011
10/18 Neural Architecture Search Slides Zoph and Le 2017
Fall Break
Quiz on First Half of Semester 10/28 Review Project Proposal
10/30 Checklist 01 Quiz 01
Guest Speakers
AI Research at Notre Dame 11/1 Adam Czajka on Biometric Recognition from the Human Iris
11/4 Aparna Bharati on Detecting Disinformation on the Internet with AI
Structure of the Brain
Connectomics 11/6 Anatomical Imaging in Neuroscience Slides Kasthuri et al. 2015; Homework 05
11/8 Segmentation of Neural Volumes: Classical Approaches Slides Arganda-Carreras et al. 2015
11/11 Segmentation of Neural Volumes: Flood-filling Networks Slides Li et al. 2019
Artificial Neural Networks with Anatomical Fidelity 11/13 Networks of the Brain Slides Sporns 2011
11/15 Recurrence in Artificial and Biological Networks Slides Kietzmann et al. 2018; Homework 06
11/18 Connectome-Based Artificial Neural Networks Slides Tschopp et al. 2018; Project Update
Function of the Brain
Artificial Neural Networks with Functional Fidelity 11/20 Functional Imaging in Neuroscience Slides Zoccolan et al. 2009
11/22 Internal Behavior of Artificial and Biological Networks Slides Yamins et al. 2013
11/25 Models of Neural Network Dynamics Slides Paugam-Moisty 2006; Homework 07
11/27 Thanksgiving Break
11/29 Thanksgiving Break
Bayesian Statistics for Decision Making
Probabilistic Read-Out Layers for Artificial Neural Networks 12/2 Bayes' Theorem and Knowledge Representation Slides R&N 14.1-14.3
12/4 Bayesian Hierarchical Modelling Slides Allenby et al. 2005; Homework 08
12/6 Combining Bayesian Models with Artificial Neural Networks Slides Campero et al. 2017
Quiz on Second Half of Semester 12/9 Review
12/11 Checklist 02 Quiz 02
Final Project 12/18 Project Due


Component Points
Participation Participation in class, film response, office hours, and slack chats. 100
Homeworks Homework assignments. 8 × 100
Project Final group project. 700
Quizzes In-class quizzes. 2 × 200
Total 2000


Grade Points Grade Points Grade Points
A 1860-2000 A- 1800-1859
B+ 1734-1799 B 1666-1733 B- 1600-1665
C+ 1534-1599 C 1466-1533 C- 1400-1465
D 1300-1399 F 0-1299

Due Dates

All Homeworks are to be submitted to your own private GitLab repository. Unless specified otherwise:

  • Homeworks are due by 11:59pm one week following the release of the assignment.

  • Project deliverables are due by 11:59pm on the assigned deadline.



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.

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, a late penalty, as determined by the instructor, will be assessed to any late submission of an assignment. In general, the late penality is -10 points off for each day after the assigned deadline. The instructor reserves the right to refuse any unexcused late work.

Classroom Recording

Notre Dame has implemented a classroom recording system. This system allows us to record and distribute lectures to you in a secure environment. You can watch these recordings on your computer, tablet, or smartphone. The recordings can be accessed within Sakai.

Because we will be recording in the classroom on select occasions, your questions and comments may be recorded. (Video recordings typically only capture the front of the classroom.) If you have any concerns about your voice or image being recorded, please speak to me to determine an alternative means of participating. No content will be shared with individuals outside of your course without your permission except for faculty and staff that need access for support or specific academic purposes.

These recordings are jointly copyrighted by the University of Notre Dame and your instructor. Posting them to other websites, including YouTube, Facebook, Vimeo, 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 may discuss with other students and consult printed and online resources. You may quote from books and online sources as long as you cite them properly. However, you may not look at another student's solution, and you may not copy solutions.

For further guidance please refer to the CSE Honor Code or ask the instructor.


Artificial Intelligence: A Modern Approach

Peter Norvig and Stuart J. Russell