CS 188 | Introduction to Artificial Intelligence

Summer 2020

Lectures: M-Th 12:30 pm - 2 pm

CS188 Robot Waving

Practice resources for Midterm 1 can be found here

Description

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.

By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to AIMA unless otherwise specified.


Syllabus

Slides from previous semesters (denoted archive) are available before lectures - official slides will be uploaded following each lecture.

W Date Lecture Topic Readings Section Homework Project
1 M 6/22 Intro to AI pdf pptx webcast Ch. 1 & 2 Worksheet 1 Solutions 1
HW0 (Due 6/24) HW1 (Due 6/26) Written Assessment 1 (Due 6/29)
P0: Tutorial
(Due 6/26)

T 6/23 Uninformed Search pdf pptx webcast Ch. 3.1–3.4 Note 1
W 6/24 A* Search and Heuristics pdf pptx webcast Ch. 3.5–3.6
Th 6/25 Game Trees I pdf pptx webcast Ch. 3.5–3.6, Ch. 16.1–16.3 Note 2
2 M 6/29 Game Trees II pdf pptx webcast Ch. 3.5–3.6, Ch. 16.1–16.3 Worksheet 2 Solutions 2
EP 1 EP Sols 1
Worksheet 3 Solutions 3
EP 2 EP Sols 2

HW2 (Due 7/2)
P1: Search
(Due Monday 7/6 11:59 pm)

T 6/30 MDP I pdf pptx webcast Ch. 17.1 - 17.3 Note 3
W 7/1 MDP II pdf pptx webcast Ch. 17.1 - 17.3
Th 7/2 RL I pdf pptx webcast Ch. 21; Sutton Ch. 6.1, 6.2, 6.5
Note 4
3 M 7/6 RL II  pdf pptx webcast Ch. 21 Worksheet 4 Solutions 4
EP 3 EP Sols 3
Midterm Review: Search
Midterm Review: Games
Midterm Review: MDPs
Midterm Review: RL
Search Sols
Games Sols
MDPs Sols
RL Sols

HW3 (Due 7/10)
Practice Midterm 1 (Due 7/11 Saturday 11:59 pm)
P2: Games
(Due Friday 7/10 11:59 pm)
Mini-Contest 1
(Due 7/16 Thursday 11:59 pm)
T 7/7 Probability pdf pptx webcast Ch. 13.1–13.5 Note 5
W 7/8 BN: Representation  pdf pptx webcast Ch. 14.1, 14.2, 14.4
Th 7/9 BN: Independence  pdf pptx webcast
4 M 7/13 Midterm 1 (covers through RL, i.e. Lec 9) Ch. 14.4 P3: RL
(Due Friday 7/17 11:59 pm)
Mini-Contest 2
(Due 8/5 Wednesday 11:59 pm)
T 7/14 BN: Inference
W 7/15 BN: Sampling I  
Th 7/16 BN: Sampling II  Ch. 14.4-14.5
5 M 7/20 HMM I  Ch. 15.2-15.6
Note 6
T 7/21 HMM II   Ch. 15.2, 15.6
W 7/22 DN/VPI  Note 7
Th 7/23 ML: Naive Bayes  Ch. 20.1 -20.2.2
Note 8
6 M 7/27 TBD: Machine Learning and Review Ch. 14.4 P4: Ghostbusters
(Due Friday 7/31 11:59 pm)
T 7/28 TBD: Machine Learning and Review
W 7/29 Midterm 2  
Th 7/30 TBD: Machine Learning and Review Ch. 18.6.3, 18.8
7 M 8/3 TBD: Machine Learning and Review Ch. 16.5 - 16.6 P5: ML
(Due Friday 8/7 11:59 pm)
T 8/4 TBD: Machine Learning and Review
W 8/5 TBD: Machine Learning and Review
Th 8/6 TBD: Machine Learning and Review  
8 M 8/10 Review Lectures and Exam Prep only  
T 8/11 Review Lectures and Exam Prep only  
W 8/12 Review Lectures and Exam Prep only  
Th 8/13 Final Exam