CS 188 | Introduction to Artificial Intelligence

Fall 2018

Lecture: Tu/Th 2:00-3:30 pm, Wheeler 150

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.



Syllabus


Wk Date Lecture Topic Readings Section Homework Project
0 8/23 Th Intro to AI
(Slides: 1PP · 2PP · 4PP · 6PP · video)
Ch. 1, 2
Note 1
No Section HW0 Math Diagnostic P0 Tutorial
1 8/28 Tu Uninformed Search
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 3.1-4 Section 1 (without solutions) HW1 Search
[Electronic+ Written]
(Both due 9/4 11:59pm) [Written solutions]
8/30 Th A* Search and Heuristics
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 3.5-6
2 9/4 Tu CSPs I
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 6.1
Note 2
Section 2 (without solutions) HW2 CSPs
[Electronic+ Written]
(Both due 9/10 11:59pm) [Written solutions]
P1 Search
(Due 9/7 4pm)

Mini-Contest 1
(Due 9/16 11:59pm)
9/6 Th CSPs II
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 6.2-5
3 9/11 Tu Game Trees: Minimax
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 5.2-5
Note 3
Section 3 (without solutions) HW3 Games
[Electronic+ Written]
(Both due 9/17 11:59pm) [Written solutions]
9/13 Th Game Trees: Expectimax, Utilities
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 5.2-5, 16.1-16.3
4 9/18 Tu MDPs I
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 17.1-3
Note 4
Section 4 (without solutions) HW4 MDPs
[Electronic+ Written]
(Both due 9/24 11:59pm) [Written solutions]
P2 Games
(Due 9/21 4pm)

Mini-Contest 2
(Due 9/30 11:59pm)
9/20 Th MDPs II
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX · video)
Ch. 17.1-3, Sutton and Barto Ch. 3-4
5 9/25 Tu RL I
(Slides: 1PP · 2PP · 4PP · 6PP · PPTX)
Ch. 21, Sutton and Barto Ch. 6.1,2,5
Note 5
Section 5 HW5 RL
(Due 10/1)
9/27 Th RL II Ch. 21
6 10/2 Tu Probability Ch. 13.1-5 Section 6 - P3 RL
(Due 10/5 4pm)
10/4 Th BNs: Representation Ch. 14.1-2,4
7 10/9 Tu Midterm 1 (7:30 - 9:30 pm)
No lecture
Section 7 HW6
(Due 10/15)
10/11 Th BNs: Independence Ch. 14.3, Jordan 2.1
8 10/16 Tu BNs: Inference Ch. 14.4 Section 8 HW7
(Due 10/22)
10/18 Th BNs: Sampling Ch. 14.4-5
9 10/23 Tu Decision Networks / VPI Ch. 16.5-6 Section 9 HW8
(Due 10/29)
10/25 Th HMMs Ch. 15.2,5
10 10/30 Tu Particle Filtering and Apps of HMMs Ch. 15.2,6 Section 10 HW9
(Due 11/5)
P4 Ghostbusters
(Due 11/2 4pm)
11/1 Th ML: Naive Bayes Ch. 20.1-20.2.2
11 11/6 Tu ML: Perceptrons Ch. 18.6.3 Section 11 HW10
(Due 11/12)
11/8 Th ML: Kernels and Clustering Ch. 18.8
12 11/13 Tu ML: Neural Networks and Decision Trees - Section 12 -
11/15 Th Midterm 2 (7:30 - 9:30 pm)
No lecture
-
13 11/20 Tu Robotics / Language / Vision - Section 13 HW 11
(Due 11/26)
11/22 Th Thanksgiving -
14 11/27 Tu Robotics / Language / Vision - Section 14 - P5 Classification
(Due 11/30 4pm)
11/29 Th Advanced Topics and Final Contest -
15 12/4 Tu Dead Week - - -
12/6 Th Dead Week -
16 12/11 Tu Final Exam (8 - 11 am) - - -