CS 188 | Introduction to Artificial Intelligence

Summer 2020

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

CS188 Robot Waving

Practice resources for the Final 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)
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 Worksheet 5 Solutions 5
Worksheet 7 Solutions 7
EP 6 EP Sols 6

HW4 (Due 7/17)
P3: RL
(Due Friday 7/17 11:59 pm)
Mini-Contest 2
(Due 8/5 Wednesday 11:59 pm)
T 7/14 BN: Inference pdf pptx webcast
W 7/15 BN: Sampling I pdf pptx webcast  
Th 7/16 BN: Sampling II  pdf pptx webcast Ch. 14.4-14.5
5 M 7/20 Particle Filtering and HMMs   pdf pptx webcast Ch. 15.2-15.6
Note 6
Worksheet 8 Solutions 8
EP 7 EP Sols 7
Worksheet 9 Solutions 9
EP 8 EP Sols 8

Written Assesment 2 (Due 7/24) HW 5 (Due 7/24)
T 7/21 Decision Networks / VPI   pdf pptx webcast Ch. 15.2, 15.6
W 7/22 ML: Perceptron and Logistic Regression   pdf pptx webcast Note 7
Th 7/23 ML: Naive Bayes  pdf pptx webcast Ch. 20.1 -20.2.2
Note 8
6 M 7/27 ML: Neural Networks I pdf pptx webcast Note 9
Midterm Review: BN
Midterm Review: BN Sols
Midterm Review: HMM
Midterm Review: HMM Sols
Midterm Review: VPI
Midterm Review: VPI Sols
EP 10
EP 10 Sols
Practice Midterm 2 (Due 7/27)
HW 6 (Due 7/31)
P4: Ghostbusters
(Due Friday 7/31 11:59 pm)
T 7/28 ML: Neural Networks II pdf pptx webcast
W 7/29 Midterm 2 [covers through VPI; i.e. Lec 18]  
Th 7/30 ML: Neural Networks III pdf pptx webcast Ch. 18.6.3, 18.8
7 M 8/3 ML: Clustering pdf pptx webcast Ch. 16.5 - 16.6
EP 11 EP Sols 11
Worksheet 12 Solutions 12
EP 12 EP 12 Sols
Worksheet 13 Solutions 13
HW 7 (Due 8/7)
Written Assessment 3 (Due 8/11)
P5: ML
(Due Friday 8/7 11:59 pm)
T 8/4 ML: Decision Trees pdf pptx webcast
W 8/5 TBD: Advanced Topics webcast
Th 8/6 TBD: Advanced Topics webcast  
8 M 8/10 Review Lectures and Exam Prep only pdf pptx webcast  
Final Review: NN, Logistic Regression
Final Review: MLE, Backpropagation
Final Review: HMMs, Viterbi
Final Review: VPI, DN
Final Review: Naive Bayes, Perceptron
Final Review: DT, Clustering
NN, Logistic Regression Sols
MLE, Backpropagation Sols
HMMs, Viterbi Sols
VPI, DN Sols
Naive Bayes, Perceptron Sols
DT, Clustering Sols
T 8/11 Review Lectures and Exam Prep only pdf pptx webcast  
W 8/12 Review Lectures and Exam Prep only webcast  
Th 8/13 Final Exam