CSCI 315: Artificial Intelligence

CSCI 315: Artificial Intelligence through Deep Learning

  Professor: Simon D. Levy
  Textbook: Buduma, Buduma, & Papa: Fundamentals of Deep Learning, 2nd edition (available through library)

What I cannot create, I do not understand.Richard Feynman 

Objectives

The goal of this course is to give you the skills and knowledge to participate in the exciting field of AI / Deep Learning. For the first half of the course you will learn to design, train, and test neural networks using the NumPy package in Python. In the second half of the course you will learn how to use the popular PyTorch Python package to train and test much more powerful deep-learning networks that exploit the Graphical Processing Unit (GPU) available on our computers. In addition to being able to design, train, and test Deep Learning networks, you will gain an understanding of the history and philosophy of AI, the current challenges it faces, and the prospects for the future.

AI Policy

Since this course is about AI, it wouldn’t make much sense to prohibit you from using AI for help on the assignments.  On the other hand, if you are getting most of the code from Claude or ChatGPT, you won’t be learning the kind of skills mentioned in the Objectives section above.   A reasonable compromise is to ask you to provide a link in your comments to whatever resources you use for help with the assignments (and unless you are paying for a subscription AI service, those links are likely to come from Stack Overflow and similar traditional help sites that I and other professionals coders are using anyway).  In the bigger picture, I’ve found that students who come to office hours / extra-help sessions and work directly with me tend to do better in my courses than students who try to find answers online.   A good way to trigger an honor violation will be to avoid getting help and then turning in a perfect solution with no mention of where the code came from. 

Attendance and Preparation

I look at this course as preparation for professional work in a research or industry setting, and I expect you to act professionally: show up for every class, participate fully, and submit your work on time without excuses. Consistent with our university’s mission statement, I expect everyone to conduct themselves with honor, integrity, and civility: if you are talking, texting, or otherwise causing a distraction in class, I will ask you to leave.

Accommodations

Washington and Lee University makes reasonable academic accommodations for qualified students with disabilities. All undergraduate accommodations must be approved through the Office of the Dean of the College. Students requesting accommodations for this course should present an official accommodation letter within the first two weeks of the (fall or winter) term and schedule a meeting outside of class time to discuss accommodations. It is the student’s responsibility to present this paperwork in a timely fashion and to follow up about accommodation arrangements. Accommodations for test-taking should be arranged with the professor at least a week before the date of the test or exam.

Washington and Lee University makes reasonable academic accommodations for qualified students with disabilities. All undergraduate accommodations must be approved through the Office of the Dean of the College. Students requesting accommodations for this course should present an official accommodation letter within the first two weeks of the (fall or winter) term and schedule a meeting outside of class time to discuss accommodations. It is the student’s responsibility to present this paperwork in a timely fashion and to follow up about accommodation arrangements. Accommodations for test-taking should be arranged with the professor at least a week before the date of the test or exam.

e University makes reasonable academic accommodations for qualified students with disabilities. All undergraduate accommodations must be approved through the Office of the Dean of the College. Students requesting accommodations for this course should present an official accommodation letter within the first two weeks of the (fall or winter) term and schedule a meeting outside of class time to discuss accommodations. It is the student’s responsibility to present this paperwork in a timely fashion and to follow up about accommodation arrangements. Accommodations for test-taking should be arranged with the professor at least a week before the date of the test or exam.

Grading

    • Two hour-long in-class exams: 30%
    • Final exam (take-home): 20%
    • Programming assignments (done individually): 50%

All work should be submitted through Github as Python .py files. The fast pace of the course means that no late work can be accepted.  The only three exceptions to this rule are:

    • Varsity sports commitments, with prior notice
    • Academic conference commitments, with prior notice
    • Serial medical / family / personal emergencies, with a adjustment from the Office of the Dean.

Because of the rapid pace of the course, I will not accept late work without prior notice (medical adjustment, conference travel, etc.)  You will lose 10% per day for each late assignment.

The grading scale will be 93-100 A; 90-92 A-; 87-89 B+; 83-86 B; 80-82 B-; 77-79 C+; 73-76 C; 70-72 C-; 67-69 D+; 63-66 D; 60-62 D-; below 60 F.

Accommodations

Washington and Lee University makes reasonable academic accommodations for qualified students with disabilities. All undergraduate accommodations must be approved through the Office of the Dean of the College. Students requesting accommodations for this course should present an official accommodation letter within the first two weeks of the (fall or winter) term and schedule a meeting outside of class time to discuss accommodations. It is the student’s responsibility to present this paperwork in a timely fashion and to follow up about accommodation arrangements. Accommodations for test-taking should be arranged with the professor at least a week before the date of the test or exam.

Schedule, Including Due Dates and On-line Class Notes

For each exam, you are responsible for all lecture-slide material posted before that exam.

Monday Wednesday Friday
5 Jan Week 0     Course OutlineWhat is (A)I?The Myth of a Superhuman AIThe Mythology of Conscious AIWill AI steal our jerbs?
12  Jan
Week 1
What is (A)I? Deep Learning Intro Article Linear Least Squares
19 Jan Week 2 Martin Luther King Day; no classes Perceptron Learning Reading:  Buduma Chapter 1Due: Assignment #1
26 Jan
Week 3
Continue: Perceptron Learning Limits of Perceptrons Limits of Perceptrons
2 Feb
Week 4
Back-propagation with hidden unitsReading: Buduma Chapter 2 Back-prop II: ImprovementsBackprop: One Weird TrickBackprop Cheat Sheet Review for Exam #1Due: Assignment #2
9 Feb
Week 5
Exam #1 Discuss Exam #1 Reading: Buduma Chapter 4Logistic Regression & Soft-Max
16  Feb
Week 6
Logistic Regression & Soft-MaxReading: Buduma Chapter 3 Logistic Regression & Soft-Max Intro to PyTorchpytorch.pyDue: Assignment #3
2 Mar
Week 7
Intro to PyTorch PyTorchReading: Buduma Chapter 3 Guest lecture by Tom Marcais
9  Mar
Week 8
PyTorch IIDue: Assignment #4 PyTorch II Review for Exam #2
16 Mar
Week 9
 Exam #2 Discuss Exam #2 Intro to Convolutional NetworksReading: Buduma Chapter 5
23 Mar
Week 10
Intro to Convolutional NetworksReading: Buduma Chapter 5 Recurrent Networks Recurrent NetworksDue: Assignment #5
30 Mar
Week 11
Recurrent NetworksReading: Buduma Chapter 7Reading: Understanding LSTM Networks Autoencoder networks Attention / Transformers Attention Is All You Need
6 Apr
Week 12
ChatGPT introGeoff Hinton Interview Attention Review for Final Exam
13 Apr
Finals Week
Due: Assignment #6