CS 188
 Introduction to Artificial Intelligence
Summer 2020
Lectures: MTh 12:30 pm  2 pm
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 decisiontheoretic 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) MiniContest 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) MiniContest 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. 14.414.5 
5 
M 7/20 
Particle Filtering and HMMs pdf pptx webcast 
Ch. 15.215.6 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 
