CS 188 | Introduction to Artificial Intelligence
Summer 2019
Lectures: M/Tu/W/Th 12:30–2:00 pm, Evans 10
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
| W | Date | Lecture Topic | Readings | Section | Homework | Project |
|---|---|---|---|---|---|---|
| 1 | Mon 6/24 |
1.
Introduction
Slides |
Ch. 1, 2 |
Section 1
Blank Sol |
HW1 Search
Electronic Written (Due 6/28 11:59 pm) |
P0 Tutorial (Optional) (Due 6/28 4 pm) |
| Tue 6/25 |
2.
Uninformed Search
Slides |
Ch. 3.1–3.4
Note 1 |
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| Wed 6/26 |
3.
A* Search and Heuristics
Slides |
Ch. 3.5–3.6 |
Section 2
Blank Sol |
|||
| Thu 6/27 |
4.
Game trees
Slides |
Ch. 5.2-5.5, Ch. 16.1-16.3 Note 2 |
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| 2 | Mon 7/1 |
5.
Game trees and MDPs I
Slides |
Ch. 17.1-17.3
Note 3 |
Section 3
Blank Sol |
HW2 Game Trees
Electronic Written HW1 Self-grade (Due 7/2 11:59 pm) |
P1
Search
(Due 7/2 4 pm) |
| Tue 7/2 |
6.
MDPs II
Slides |
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| Wed 7/3 |
7.
RL I
Slides |
Ch. 21
Note 4 |
Section 4
Blank Sol |
|||
| Thu 7/4 | Holiday (Independence Day) | |||||
| 3 | Mon 7/8 |
8.
RL II
Slides |
Section 5
Blank Sol |
HW3 MDPs & RL
Electronic Written HW2 Self-grade (Due 7/12 11:59 pm) |
P2
Games
(Due 7/12 4 pm) Mini-Contest 1 (Due 7/8 11:59 pm) |
|
| Tue 7/9 |
9.
RL III
Slides |
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| Wed 7/10 |
10.
Probability
Slides |
Ch. 13.1-13.5
Note 5 |
Section 6
Blank Sol |
|||
| Thu 7/11 |
11.
BNs: Representation I
Slides |
Ch. 14.1, 14.2, 14.4 | ||||
| 4 | Mon 7/15 |
Midterm 1 (12:30 – 2pm) Midterm 1 Prep Exam Solutions |
Section 7
Blank Sol |
P3
RL
(Due 7/19 4 pm) |
||
| Tue 7/16 |
12.
BNs: Representation II
Slides |
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| Wed 7/17 |
13.
BNs: Inference
Slides |
Ch. 14.4 |
Section 8
Blank Sol |
|||
| Thu 7/18 |
14.
BNs: Sampling I
Slides |
Ch. 14.4–14.5 | ||||
| 5 | Mon 7/22 |
15.
BNs: Sampling II
Slides |
Section 9
Blank Sol |
HW4 Probability & Bayes Nets
Electronic Written HW3 Self-grade (Due 7/23 11:59 pm) |
Mini-Contest 2
(Due 7/26 11:59 pm) |
|
| Tue 7/23 |
16.
Particle Filtering and HMMs I
Slides |
Ch. 15.2, 15.6
Note 6 |
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| Wed 7/24 |
17.
Particle Filtering and HMMs II
Slides |
Section 10
Blank Sol |
||||
| Thu 7/25 |
18.
Decision Networks / VPI
Slides |
Ch. 16.5-16.6
Note 7 |
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| 6 | Mon 7/29 |
19.
ML: Naive Bayes
Slides Slides (Annotated) |
Ch. 20.1-20.2.2
Note 8 |
Section 11
Blank Sol |
HW5 HMMs, Particle Filtering & Decision Networks
Electronic Written HW4 Self-grade (Due 7/30 11:59 pm) |
P4
BNs and HMMs
(Due 8/2 4 pm) |
| Tue 7/30 |
20.
Guest Lecture
Slides |
Ch. 18.6.3 | ||||
| Wed 7/31 |
Midterm 2 (12:30 – 2pm) Midterm 2 Prep Exam Solutions |
Section 12
Blank Sol |
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| Thu 8/1 |
21.
ML: Perceptrons
Slides Slides (Annotated) |
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| 7 | Mon 8/5 |
22.
ML: Kernels and Clustering
Slides Slides (Annotated) |
Section 13
Blank Sol |
HW6 Perceptrons
Electronic Written HW5 Self-grade (Due 8/9 11:59 pm) |
P5
Machine Learning
(Due 8/9 4 pm) Final Contest (Due 8/12 11:59 pm) |
|
| Tue 8/6 |
23.
ML: Neural Networks I
Slides Slides (Annotated) |
Ch 18.3, 18.7
Note 9 |
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| Wed 8/7 |
24.
ML: Neural Networks II
Slides Slides (Annotated) |
Section 14
Blank Sol |
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| Thu 8/8 | 25. Math/ML Review | |||||
| 8 | Mon 8/12 | 26. Review Lecture I | ||||
| Tue 8/13 | 27. Review Lecture II | |||||
| Wed 8/14 |
Final (5pm – 8pm @ VLSB 2050) Final Prep |