Artificial Intelligence

Introduction to the field of AI, including knowledge representation and algorithms for search, optimization, planning, logical and probabilistic reasoning, and machine learning.

Course Information

CSCI1410 is designed for students seeking a broad understanding of Artificial Intelligence. The course surveys many topics in AI, from search and game theory to supervised learning and reinforcement learning.

Lectures take place Tuesdays and Thursdays from 9:00 am to 10:20 am eastern time. The class is offered online this semester. The instructors are Professor Amy Greenwald and Ph.D. student Enrique Areyan Viqueira.

Prerequisites and Required Materials

In Order to take CS1410, you should have already taken CS16, CS18, or CS19. You should have also taken, or can be taken concurrently, one of CS22, CS145, APMA1650, or APMA1655.

The programming language used in this course is Python. We will provide a Python primer at the start of the semester, but knowledge of Python will be required.

The course textbook is Artificial Intelligence, A Modern Approach, 3rd edition by Russell & Norvig. The textbook is available online.

To attend online lectures and TA hours, a computer is necessary, preferably with the Zoom application installed. If you need any assistance (e.g., if you need to borrow a computer from Brown), please contact the HTAs.


Lectures are held every Tuesday and Thursday, from 9am to 10:30am ET via Zoom.

Lectures are not recorded. You can access last year's recordings here.

In addition, we are providing slides and recordings from last year covering all lectures, as well as specifically catered notes from this year covering important and new concepts!

Topic Readings Activities Notes Slides Recordings
Introduction to AI (9/10) Chapter 1 & 2 -- Notes 1 Notes 2 Slides 1 Slides 2 Recording 1 Recording 2
Blind Search (9/15) Chapter 3 Handout 1 Handout 2 Notes 1 Notes 2 Slides Recording 1
Informed Search (9/17) Chapter 3 Handout 1 Handout 2 Notes 1 Notes 2 -- --
Adversarial Search (9/22) Chapter 5 -- Notes Slides Recordings
Zero-sum Games (9/24) -- Handout
Notes 1 Notes 2 Slides (Optional) Recordings (Optional) Youtube (start at 7 min)
Optimization & Local Search (9/29) Chapter 4 Code Notes 1 Notes 2 Slides (Optional) Recordings (Optional)
Constraint Satisfaction & Satisfiability (10/1) Chapter 6 & 7 Code
iPad Notes
Notes 1 Notes 2 Slides Recordings
Responsible AI (10/6) -- -- Notes Slides Recording
Bayesian Networks (10/8) Chapter 13 & 14 Handout
iPad Notes
Notes 1 Notes 2 Slides 1 Slides 2 Recording 1 Recording 2
Hidden Markov Models (10/13), (10/15) Chapter 15 Handout
iPad Notes
Notes 1 Notes 2 Slides Recording
Markov Decision Processes (10/20), (10/22) Chapter 17 iPad Notes
Value Iteration Code
Value Iteration with Function Approximation Code
Balance Beam Grid World Code
Notes 1 Notes 2 Slides Recording
Reinforcement Learning (10/27), (10/29) Chapter 21 -- Notes 1 Notes 2 Notes 3 Notes 4 Slides 1 Slides 2 Recording 1 Recording 2
Supervised Learning (11/05, 11/12) Chapter 18 Balance Beam Code 1
Balance Beam Code 2
Value Iteration Code 1
Value Iteration Code 2
Value Iteration Code 3
Notes 1 Notes 2 Slides 1 Slides 2 Recording 1 Recording 2
Unsupervised Learning Chapter 20 -- -- Slides 1 Slides 2 Recording 1 Recording 2
Multi-Armed Bandits (11/10, 12/1) Introduction to Multi-Armed Bandits (Chapter 1) -- Notes 1 Notes 2 -- Recording
Deep Learning, Guest Lecture by Kenny Jones (11/17) -- -- -- -- Recording
Natural Language Processing, Guest Lecture by Charlie Lovering (11/19) Chapter 23 -- -- -- Recording
Fairness in Machine Learning, Guest Lecture by Cristina Meghnini (11/24) --- -- -- -- Recording


Assignments are due at 11:59PM ET on their respective due dates. Written portions and resubmissions are due the following day at 11:59PM ET.

For assignment related questions and announcements, check Piazza.

Homework Out Due
Search 9/16 9/28
Adversarial Search 9/30 10/12
Hidden Markov Models 10/21 11/2
Reinforcement Learning 11/4 11/16
Supervised Learning 11/18 11/30
Final Project 11/30 12/14


The resources below link to course policies and other useful supplementary materials.

Diversity and Inclusion

The computer science department is committed to diversity and inclusion, and strives to create a climate conducive to the success of women, students of color, students of any sexual orientation, and any other students who feel marginalized for any reason. Likewise, our course takes pride in providing an inclusive environment, and welcomes students of all backgrounds. If you feel you have been mistreated by another student, or by a member of the course staff, consider reaching out to one of student advocates on the CS department’s Diversity and Inclusion Committee, or to Professor Greenwald or Professor Cetintemel (the CS department chair). We, the CS department, take all complaints seriously.


If you feel you have any disabilities that could affect your performance in the course, please contact SEAS, and ask them to contact the course staff. We will support accommodations recommended by SEAS.


Please review Brown’s Title IX and Gender Equity Policy. If you feel you might be the victim of harassment (in this course or any other), you may seek help from any of the resources listed here.


Please check the calendar for the latest times and the most updated schedule.

Join the queue using SignMeUp when signing up for hours.


Amrita Sridhar (ETA)

Andrew Kim (TA)

Daniel Kotroco (TA)

Eli Zucker (TA)

Ella Liang (TA)

Estelle Han (TA)

Ethan Chung (TA)

George Hu (TA)

Jason Crowley (TA)

Jefferson Bernard (TA)

Jinwoo Choi (TA)

Matt Rodrigues (TA)

Matthew Kovoor (TA)

Nathan Tung (TA)

Neil Sehgal (ETA)

Shekar Ramaswamy (TA)

Shreya D'Souza (TA)

Will Hackett (TA)

Husam Salhab (HTA)

Amy Greenwald (Professor)

Enrique Areyan Viqueira (Professor)

Soma Arunkanti Hota (HTA)