CS 188 | Introduction to Artificial Intelligence

Spring 2019

Lecture: M/W 5:00-6:30 pm, Wheeler 150

CS188 Robot Waving

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


Wk Date Lecture Topic Readings Section Homework Project
0 1/23 Wed Intro to AI
(Slides: PDF — Video)
Ch. 1 & 2
Note 1
N/A HW0 Math Diagnostic
[Electronic]
(Due 1/28 11:59 pm)
P0 Tutorial
(Due 1/28 4 pm)
1 1/28 Mon Uninformed Search
(Slides: PDFPPTX — Video)
Ch. 3.1-3.4 Section 1 (video) (without solutions) HW1 Search
[Electronic] [Written]
(Due 2/4 11:59 pm)
[Written solutions]
1/30 A* Search and Heuristics
(Slides: PDFPPTX — Video)
Ch. 3.5-3.6
2 2/4 Mon Game Trees
(Slides: PDFPPTX — Video)
Ch. 5.2-5.5, Ch. 16.1-16.3
Note 3
Section 2 (video) (without solutions) HW2 Game Trees
[Electronic] [Written]
(Due 2/11 11:59 pm)
[Written solutions]
P1 Search
(Due 2/8 4 pm)

Mini-Contest 1 (Due 2/11 11:59 pm)
2/6 Wed MDPs I
(Slides: PDFPPTX — Video)
Ch. 17.1-17.3
Note 4
3 2/11 Mon MDPs II
(Slides: PDFPPTX — Video)
Ch. 17.1-17.3, Sutton and Barto Ch. 3 & 4 Section 3 (video) (without solutions) HW3 MDPs
[Electronic] [Written]
(Due 2/18 11:59 pm)
[Written solutions]
2/13 Wed RL I
(Slides: PDFPPTX — Video)
Ch. 21, Sutton and Barto Ch. 6.1, 6.2 & 6.5
Note 5
4 2/18 Mon Holiday Section 4 (without solutions) HW4 RL
[Electronic] [Written]
(Due 2/25 11:59 pm)
P2 Games
(Due 2/22 4 pm)

Mini-Contest 2 (Due 3/11 11:59 pm)
2/20 Wed RL II
(Slides: PDF — Video)
Ch. 21
5 2/25 Mon CSPs I Ch 6.1
Note 2
Section 5 HW5 CSPs
[Electronic] [Written]
(Due 3/4 11:59 pm)
2/27 Wed CSPs II Ch 6.2-6.5
6 3/4 Mon Propositional Logic TBC Section 6 HW6 Logic
[Electronic] [Written]
(Due 3/11 11:59 pm)
P3 RL
(Due 3/8 4 pm)
3/6 Wed First-Order Logic TBC
7 3/11 Mon Probability Ch 13.1-13.5 Section 7 HW7
[Electronic] [Written]
(Due 3/18 11:59 pm)
3/13 Wed Bayesian Networks: Representation Ch 14.1, 14.2 & 14.4
Note 6
8 3/18 Mon Bayesian Networks: Inference Ch 14.4 Section 8 HW8
[Electronic] [Written]
(Due 4/1 11:59 pm)
3/20 Wed Midterm (7 - 9 pm)
9 3/25 Mon Spring Break N/A
3/27 Wed Spring Break
10 4/1 Mon Bayesian Networks: Sampling Ch 14.4-14.5 Section 10 HW9
[Electronic] [Written]
(Due 4/8 11:59 pm)
P4 Bayesian Networks and Hidden Markov Models
(Due 4/12 4 pm)
4/3 Wed Bayesian Networks: Sampling Ch 14.4-14.5
11 4/8 Mon Decision Networks / Value of Perfect Information Ch. 16.5-16.6
Note 7
Section 11 HW10
[Electronic] [Written]
(Due 4/15 11:59 pm)
4/10 Wed Particle Filtering and Hidden Markov Models Ch. 15.2-15.6
Note 8
12 4/15 Mon Machine Learning: Naive Bayes Ch. 20.1-20.2.2
Note 9
Section 12 HW11
[Electronic] [Written]
(Due 4/24 11:59 pm)
4/17 Wed Machine Learning: Perceptrons Ch. 18.6.3
13 4/22 Mon Machine Learning: Kernels and Clustering Ch 18.8 Section 13
4/24 Wed Machine Learning: Neural Networks and Decision Trees Ch 18.3 & 18.7
Note 10
14 4/29 Mon Robotics / Language / Vision N/A Section 14 P5 Classification
(Due 5/3 4 pm)
5/1 Wed Advance Topics and Final Contest N/A
15 5/6 Mon RRR Week N/A
5/8 Wed RRR Week
16 5/13 Mon Finals Week N/A
5/15 Wed Finals Week