CS33 Foundations of AI and Machine Learning (Fall 2026)

Still under construction

Course Information

Schedule
Section 1: Meeden: MW 10:30-11:45 Martin 204
Section 2: Mitchell: TR 11:20-12:35 Sci 199
Lab A: Meeden R 1:05-2:35 Martin 213
Lab B: Mitchell R 1:05-2:35 Martin 313
Lab C: Kazer F 2:00-3:30 Martin 313
Lab D: Kazer F 3:45 - 5:15 Martin 313

Contact Information
Professor: Lisa Meeden
Email: lmeeden at swarthmore.edu
Office: Martin 208
Office Hours: TBD
Professor: Ben Mitchell
Email: mitchell at cs.swarthmore.edu
Office: Martin 334
Office Hours: TBD
Lab Instructor: Charlie Kazer
Email: ckazer at cs.swarthmore.edu
Office: Martin 330
Office Hours: TBD

Introduction

This course provides a broad introduction to the foundations of modern artificial intelligence (AI) and will also develop advanced programming and data processing skills essential for upper-level courses in computer science. Topics will include advanced Python programming, mathematical foundations, data processing, machine learning methodology, neural networks, and large language models. Throughout the course, students will examine the ethical issues that arise in AI and its societal impacts. The course culminates in a final project. Students will gain core skills that will prepare them for a deeper examination of these issues in both later courses and independent projects.

The course is suitable for any student interested in gaining a deeper understanding of AI regardless of their intended major.

Prerequisite: Completion of CPSC 21 or its equivalent.

Goals for student learning

Grading

Grade weighting
?%Class Participation
?%Labs
?%Exam 1
?%Exam 2
?%Final Project

Reading

Rather than using a single textbook, we will be using materials from a variety of sources. Many of the materials will be available online.

Policies

Labs

Academic Integrity

Academic Accommodations


Schedule

WEEK DAY ANNOUNCEMENTS TOPIC & READING LAB
1

Sep 01

  

Overview of AI and Machine Learning

  • Consequences of using automated systems in the real world
  • Motivating example: Self-driving cars

Lab 1: Critiquing generative AI models

Sep 03

  
2

Sep 08

  

Data Science

  • Pre-processing data
  • Feature engineering
  • Data visualization
  • Basic statistics

Lab 2: Advanced Python

Sep 10

  
3

Sep 15

  

Introduction to Modeling

  • What is a model?
  • Determining a model's fitness
  • Complexity of a model

Lab 3: Data cleanup and analysis using pandas

Sep 17

  
4

Sep 22

  

Classification, Logistic Regression

Lab 4: Logistic Regression

Sep 24

  
5

Sep 29

  

Ethical issues that arise with data

  • Data provenance
  • Data diversity
  • Data imbalance
  • How data is applied to solve problems

Lab 5: Explore issues with data in a model

Oct 01

  
6

Oct 06

  

Neural Networks

  • Backpropagation

Lab 6: Implementing Backprop

Oct 08

  
 

Oct 13

Fall Break

Oct 15
7

Oct 20

  

Dimensionality Reduction

  • Principal components analysis
  • Visualizing hidden layers

Lab 7: Use PCA on models

Oct 22

  
8

Oct 27

  

Evaluating Models

  • Overfitting and Underfitting
  • Hyperparameter tuning
  • Possible harms

Lab 8: Evaluate some models

Oct 29

  
9

Nov 03

  

Advanced Neural Networks that Process Text

  • Generative vs Discriminative Models
  • LLMs and Transformers
  • Prediction

Lab 9: Sentiment analysis with LLMs

Nov 05

  
10

Nov 10

  

Advanced Neural Networks that Process Images

  • Generative Models

Lab 10: Image generation

Nov 12

  
11

Nov 17

  

Project Development

  • Provide limited set of possibilities
  • Staged development plan
  • Deliverable (paper/poster/presentation)

Project Proposal

Nov 19

  
12

Nov 24

  

Societal Implications of ML Models

  • Disparate Impact
  • Transparency/explainability
  • Stakeholders

Project Feedback

Nov 26

Thanksgiving Break
13

Dec 01

  

More on Societal Implications

Project Continues

Dec 03

  
14

Dec 08

  

TBD

Finish Project