01:12:38

Review for the Final Fall 2014

01:19:39

Lecture 11/25/14

01:17:43

CS 141: James MacGlashan guest lecture on Reinforcement Learning & Reward Functions

01:18:53

AI Lecture 11/18/14

01:16:21

Lecture 11 4

01:12:41

CS141: Lecture 15 - Classification (10/30/14)

01:16:28

AI Lecture 10/28/14

01:17:34

CS141: Lecture 13 - Graphical Models & Slam (10/16/14)

01:07:09

SLAM tutorial

01:07:09

SLAM

01:20:17

Brown AI Lecture 10/14/14

01:00:31

CS141: Lecture 9 - Betsy on AI & Auctions (10/2/14)

01:17:34

AI Lecture 9/30/2014

01:16:24

CS141: Lecture 7 - Adversarial Search (9/25/14)

01:18:42

Brown CS 141 AI Lecture 9/23/14

01:16:22

Brown CS 141 AI Lecture 9/18/14

01:17:02

CS141: Lecture 4 - Local Search (9/16/14)

01:16:42

Lecture 3 Brown CS 141

01:17:01

CS141: Lecture 1 -- Introduction (9/4/14)

50:18

CS141: Lecture 2 -- Planning and Search (9/9/14)

01:01:46

Final Review

01:06:09

Lecture 22: Robotics (part 2)

01:14:34

Lecture21: Robotics Part 1

01:18:29

Lecture 20: Rl - RMax, Policy Search, and Deep RL

01:12:27

Lecture 19: Bandit Problems

01:16:12

Lecture 18: RL Part 1: Q-Learning

01:16:13

Lecture 16: Application: Computer Vision

46:35

Lecture 15: Support Vector Machines

02:25:39

Lecture 14: Naive Bayes and Logistic Regression

01:16:45

Lecture 13: Decision Making Under Uncertainty

57:15

Peter Norvig Q&A

01:13:40

Midterm Review

01:04:31

Lecture 12: Simultaneous Localization and Mapping (SLAM)

01:14:14

Lecture 11: Bayes' Filters

01:20:49

Lecture 10: Conditional Probability and Bayes Nets

01:16:55

Lecture 9: The Quantitative Rules

01:19:27

Lecture 8: Plausible Reasoning

01:18:26

Lecture 7: Adversarial Search

01:17:14

Lecture 6: Online Search Algorithms

01:20:52

Lecture 5: Application: Following Tech Support Articles

01:21:11

Lecture 4: Local Search

01:00:17

Lecture 1: Introduction