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This schedule tentative and subject to change.

Date Topic
AIMA Textbook Reference
Lec. 1
W 2/8
Introduction to AI
Ch 1, Ch 2
Rec. 1
F 2/10
Probability Review
Probability Handout / Solutions
13.2 - 13.5
Lec. 2
M 2/13
Logic: Propositional
Project 1: Out
AIMA Lecture Slides
7.1 - 7.4
Lec. 3
W 2/15
Logic: Proof and Satisfiability
AIMA Lecture Slides
7.5 - 7.7
Rec. 2
F 2/17
Logic
Logic Handout / Solutions
Lec. 4
T 2/21
Mon Sched
Probabilistic Inference
AIMA Lecture Slides
13.3 - 13.6
Lec. 5
W 2/22
Bayes Nets: Representation
Project 1: Due, Project 2: Out
AIMA Lecture Slides
14.1 - 14.2
Rec. 3
F 2/24
Bayes Nets
Bayes Nets Handout / Solutions
Lec. 6
M 2/27
Bayes Nets: Independencies
Lecture Slides
Bayes Nets Practice Problems
14.4
Lec. 7
W 2/29
Bayes Nets: Approximate Inference
Project 2A: Due
AIMA Lecture Slides
14.5
Rec. 4
F 3/2
Bayes Nets, Approximate Inference
Bayes Nets Handout 2 / Solutions
Lec. 8
M 3/5
HMMs
AIMA Lecture Slides
15.1 - 15.3
Midterm 1
W 3/7
MIDTERM 1 (In class)
Quiz 1 / Solutions
Rec. 5
F 3/9
HMMs and Search Review
HMMs and Search Review Handout
3.3 - 3.4
Lec. 9
M 3/12
Planning and Search
Lecture Slides
3.5 - 3.7, 10.1 - 10.2
Lec. 10
W 3/14
Planning and Search
Project 2B: Due, Project 3: Out
Lecture Slides
3.5 - 3.7, 10.1 - 10.2
Rec. 6
F 3/16
Planning and Search
Planning and Search Handout / Solutions
Lec. 11
M 3/19
Search: Motion planning
Lecture Slides
Lec. 12
W 3/21
Search: CSP
Project 3A: Due
Lecture Slides
6.1 - 6.6
Rec. 7
F 3/23
Motion Planning and CSPs
Motion Planning and CSPs Handout / Solutions
Break
M 3/26-3/30
SPRING BREAK
Lec. 13
M 4/2
Adversarial Search
Lecture Slides
5.1-5.5
Lec. 14
W 4/4
MDP: Defintion
Project 3B: Due
Lecture Slides (part 1)
Lecture Slides (part 2)
16.1 - 16.3, 17.1
Rec. 8
F 4/6
Games & Utility
Games and Utility Handout / Solutions
Lec. 15
M 4/9
MDP: Solution
Lecture Slides
17.2 - 17.3
Lec. 16
W 4/11
Reinforcement Learning
Project 3C: Due
Lecture Slides
21.1 - 21.3
Rec. 9
F 4/13
MDPs
MDPs Handout / Solutions
Holiday
M 4/16
Patriots Day
Midterm 2
W 4/18
MIDTERM 2 (In class)
Quiz 2 / Solutions
Rec. 10
F 4/20
Linear Algebra Review
Linear Algebra Handout / Solutions
Lec. 17
M 4/23
Learning: Intro and Decision Trees
Lecture Slides
18.1 - 18.4
Lec. 18
W 4/25
Learning: KNN, Naive Bayes
Project 4: Out
Lecture Slides
18.8.1 - 18.8.3
Rec. 11
F 4/13
Learning: Decision Trees and Naive Bayes
Decision Trees and Naive Bayes Handout / Solutions
Lec. 19
M 4/30
Learning: Linear classifiers
Lecture Slides
18.6.3 - 18.6.4
Lec. 20
W 5/2
Learning: SVM
Project 4A: Due
Lecture Slides
18.9
Rec. 12
F 5/4
Classification
Classification Handout / Solutions
Lec. 21
M 5/7
Learning: Regression
Lecture Slides
18.6 - 18.8
Lec. 22
W 5/9
Learning: Wrapup
Project 4B: Due
Lecture Slides
Rec. 13
F 5/11
Regression
Regression Handout / Solutions
Lec. 23
M 5/14
Guest Lecture: Prof. Tommi Jaakkola
Lecture Slides
Lec. 24
W 5/16
Guest Lecture: Prof. Regina Barzilay
Lecture Slides
Final
M 5/21
FINAL: May 21, 9AM, Ice Rink
 
6.034 - Artificial Intelligence

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