Schedule

CS-31 | Tufts University | Fall 2026 · Tu · Th 12:00 PM – 1:15 PM

Note: This is a tentative schedule. Topics/dates are subject to change.

Unit 0: Getting Started

What even is AI anyway? What makes a computational problem "hard"?

Topics: Defining AI and Agents, Fundamentals of Algorithms

Week Assigned Do Before Tuesday Class Content Optional
Week 1
Perspectives on AI

Lecture: 9/8
Lab: 9/10

This column will contain the due dates of things you are responsible for.


Due 9/10 Before Lab:

Starting Week 2, anything in this column you should do before Tuesday lecture. For this week, read these before lab on 9/10.


Read/Listen:

This column will contain resources and info that you'll use during class.


Slides:

Lab:

  • Week 1 Lab zip (click "Download raw file", then unzip into your environment)
  • Quiz - Diagnostic. Graded for completion, no Week 1 content covered.

Some weeks, I'll put links in this column for things that are not required for the class but may be interesting and/or helpful to you.


Python Refresher:
Week 2
Fundamentals of Algorithms

Lecture: 9/15
Lab: 9/17

Due 9/17

  • Lab 01 (Gradescope)

Slides:

  • Slide Link

Lab:

  • Lab Link

Quiz:

  • Quiz 1 covers Week 1 (Big Picture and Onboarding).

Unit 1: Certain Reasoning

How do we choose intellegent actions if we know where we are and what we're doing?

Topics: State Spaces, Search Algorithms, Constraint Satisfaction, Optimization, Ethics of Automation

Week Assigned Do Before Tuesday Class Content Optional
Week 3
Searching and Planning

Lecture: 9/22
Lab: 9/24		 - - TBD -
Week 4
Backtracking Search and Constraint Satisfaction

Lecture: 9/29
Lab: 10/1		 - - TBD -
Week 5
Linear Programming and Optimization

Lecture: 10/6
Lab: 10/8		 - - TBD -
Week 6
Ethics of Automation

Lecture: 10/13
Lab: 10/15

Due 10/15:

  • Topic Exploration
 - TBD -

Unit 2: Uncertain Reasoning

How do we choose intellegent actions if we don't know where we are and what we're doing?

Topics: Fundamentals of Probability, Graphical Models, Simulations, Reinforcement Learning

Week Assigned Do Before Tuesday Class Content Optional
Week 7
Probability Fundamentals

Lecture: 10/20
Lab: 10/22		 - - TBD -
Week 8
Bayesian Networks and Monte Carlo

Lecture: 10/27
Lab: 10/29

Due 10/29:

  • Project Proposal
 - TBD -
Week 9
Markov Models and Introductory Reinforcement Learning

Lecture: 11/3
Lab: 11/5		 - - TBD -

Unit 3: Agent and Application Architectures

How do we combine various reasoning methods to create more complex applications?

Topics: Agent Architectures, Knowledge Representation, Multi-Agent Systems, Agentic GenAI and Chatbots, Alignment and Safety

Week Assigned Do Before Tuesday Class Content Optional
Week 10
Basic Agents and Agent Architectures

No Lecture on 11/10
Lab: 11/12		 - -

No Lecture on 11/10. Wednesday schedule on Tuesday.

 -
Week 11
Multi-Agent Systems and Agent-based Models

Lecture: 11/17
Lab: 11/19

Due 11/19:

  • Preliminary Workflow and Document
 - TBD -
Week 12
Agentic Chatbots and AI Safety

Lecture: 11/24
No Lab on 11/26		 - -

No Lab Session on 11/26. Enjoy your break!

 -

Finale: Wrapping It Up

Let's share what we've learned!

Week Assigned Do Before Tuesday Class Content Optional
Week 13
Slack Week / Project Support

Slack Day 1: 12/1
Slack Day 2: 12/3

Due 12/1:

  • Project Peer Reviews
 - TBD -
Week 14
Final Project Presentations

Presentation Day 1: 12/8
Presentation Day 2: 12/10		 - -

Final Project Presentations!

 -
Week 15
No Lecture (Reading Period): 12/15
Optional Final Exam: 12/16

Due 12/15:

  • Final Report
 - TBD -