Copy of the class homepage from before the suspension of classes
Lecture # | Date | Topic/Slides | Readings | ||
---|---|---|---|---|---|
1 | 1/22 | Introduction to AI and the course | Skim Chapters 1 and 2 | ||
2 | 1/24 | State Space Search | Section 3.1-3.3 | ||
3 | 1/27 | Depth-First, Breadth-First Search | Section 3.4 | ||
4 | 1/29 | Depth-First, Breadth-First, Iterative Deepening Search | Section 3.4 | ||
5 | 1/31 | A* Search | Sections 3.5-3.6 | ||
6 | 2/3 | A* Search, Local Search | Section 3.6,4.1 | ||
7 | 2/5 | Adversarial Search | Sections 5.1-5.3 | ||
2/7 | Snow Day | ||||
8 | 2/10 | Adversarial Search | Sections 5.1-5.3 | ||
9 | 2/12 | Adversarial Search | Sections 5.1-5.3 | ||
10 | 2/14 | Propositional Logic | Sections 7.1-7.4 | ||
Video Link: click here | |||||
11 | 2/17 | Propositional Logic | Sections 7.4-7.5 | ||
12 | 2/19 | Propositional Logic | Sections 7.5 | ||
13 | 2/21 | Propositional Logic, First-Order Logic | Sections 7.5-7.6 | ||
14 | 2/26 | First-Order Logic | Sections 8.1-8.2, 9.1-9.2 | ||
15 | 2/28 | First-Order Logic | Sections 9.1-9.2, 9.5 | ||
16 | 3/2 | First-Order Logic, Markov-Decision Processes | Sections 9.1-9.2, 9.5, 17.1 | ||
17 | 3/4 | Markov-Decision Processes | Section 17.1 | ||
18 | 3/6 | Markov-Decision Processes | Section 17.2 | ||
19 | 3/9 | Markov-Decision Processes, Reinforcement Learning | Sections 17.2, 22.1, 22.3 | ||
Policy iteration example from class | |||||
20 | 3/11 | Reinforcement Learning | Section 22.3 | ||
21 | 3/11 | Reinforcement Learning | Section 22.3 | ||
Suspension of classes | |||||
22 | 4/6 | Multi-Armed Bandits | Section 17.3 | ||
23 | 4/8 | Multi-Armed Bandits | Section 17.3 | ||
4/9-10 | Quiz 1: Solutions | Topic: Uninformed Search | |||
Review questions: Questions 1a-c and 3a-c | |||||
Review questions: Solutions | |||||
24 | 4/10 | Monte Carlo Tree Search | Section 5.4 | ||
25 | 4/13 | Overview of Machine Learning and Supervised Learning | Sections 19.1-19.2 | ||
4/14-15 | Quiz 2: Solutions | Topic: Lectures 22-24 | |||
Review questions: Quiz 2 | |||||
Review questions: Solutions | |||||
26 | 4/15 | Supervised Learning | Section 19.4 | ||
4/16-17 | Quiz 3: Solutions | Topic: Informed search (A*, Hill Climbing, etc.) | |||
Review questions: Questions 1d-e, 2c, 3d, and 4-7 | |||||
Review questions: Solutions | |||||
27 | 4/17 | Perceptrons | Sections 19.4, 19.6 | ||
Perceptron spreadsheet from lecture: | |||||
(Create your own copy to edit) Google doc | |||||
(Download to edit): Excel spreadsheet | |||||
28 | 4/20 | Logistic Regression | Sections 19.6 | ||
4/21-22 | Quiz 4: Solutions | Topic: Lectures 25-27 | |||
Review questions: Problems and Solutions | |||||
29 | 4/22 | Neural Networks | |||
4/23-24 | Quiz 5: Solutions (Revised) | Topic: Adversarial Search | |||
Review questions: Questions 1f and 8-11 | |||||
Review questions: Solutions | |||||
See also Textbook github questions: | |||||
Chapter 5: #1, 9, 10, and 12 | |||||
30 | 4/24 | Neural Networks | |||
31 | 4/27 | Neural Networks | |||
4/28-29 | Quiz 6: Solutions | Topic: Lectures 28-30 | |||
Delayed until 4pm EDT | |||||
Review questions: Problems and Solutions | |||||
32 | 4/29 | Naive Bayes | |||
4/30-5/1 | Quiz 7: Solutions | Topic: Formal Logic | |||
Review questions: Questions 1g-j and 12-19 | |||||
See also Textbook github questions: | |||||
Chapter 7: #4, 6, 7, 9, 16 (parts 1 and 2), 20, 21, 23 | |||||
(skip questions with double-headed arrows) | |||||
Chapter 8: #10, 11, 12, 26, 27, 32, 36 | |||||
Chapter 9: #5, 8, 24 | |||||
33 | 5/1 | Naive Bayes | Section 20.2 | ||
34 | 5/4 | k-Means Clustering | https://en.wikipedia.org/wiki/K-means_clustering | ||
5/5-6 | Quiz 8: Solutions | Topic: Lectures 31-33 | |||
Review questions: Problems and Solutions | |||||
35 | 5/6 | Natural Language Processing | Chapters 23, 24 | ||
Guest lecturer: Professor Claire Cardie | |||||
5/7-8 | Quiz 9: Solutions | Topic: Reinforcement Learning | |||
Review questions: Questions 20-23 | |||||
36 | 5/8 | Computer Vision | Chapter 25 | ||
Guest lecturer: Grant Van Horn, Ph.D. | |||||
37 | 5/11 | Societal implications of AI | Chapters 27-28 | ||
5/12-13 | Quiz 10: Solutions | Topic: Lectures 34 | |||
Review questions: Problems and solutions |
Number | Assignment | Due | Late | Solutions | ||||
---|---|---|---|---|---|---|---|---|
Homework 1 | Background knowledge assessment | 1/31 11:59 PM | No late submissions | HW1 Solutions | ||||
Homework 2 | Search Algorithms | 2/17 11:59 PM | 2/19 11:59 PM | HW2 Solutions | ||||
Homework 3 | Game Trees & Logic | 4/8 11:59 PM | 4/8 11:59 PM | HW3 Written Solutions | ||||
.ipynb file | HW3 Example Programming Solution | |||||||
Homework 4 | Learning Approaches | 4/28 11:59 PM | 4/30 11:59 PM | HW4 Solutions | ||||
Homework 5 | Machine Learning | 5/12 11:59 PM | 5/14 11:59 PM | HW5 Solutions |
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