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Summer 2014 Syllabus

This syllabus is subject to change. In particular, the unreleased project out and due dates will likely change somewhat. Office hours may also move in certain weeks, see announcements for changes.

CS188 Summer Weekly Schedule

Day Topic Optional Reading Slides Assignment Due
Tue 6/24 Introduction to AI Ch. 1 1PP · 2PP 
4PP · 6PP
P0: Tutorial Wed 6/25
Wed 6/25 Uninformed Search Ch. 3.1-4 (2e: Ch. 3) 1PP · 2PP 
4PP · 6PP
HW1 Fri 6/27
Thu 6/26 A* Search and Heuristics Ch. 3.5-6 (2e: Ch. 4.1-2) 1PP · 2PP 
4PP · 6PP
P1: Search 
(section / solutions)
Mon 6/30

Mon 6/30 Constraint Satisfaction Problems Ch. 6.1 (2e: Ch. 5.1)
Backtracking Demo
1PP · 2PP 
4PP · 6PP
HW2 Wed 7/2
Tue 7/1 CSPs II Ch. 6.2-5 (2e: Ch. 5.2-4) 1PP · 2PP 
4PP · 6PP
(section / solutions)
(section / solutions)
Wed 7/2 Game Trees: Minimax Ch. 5.2-5 (2e: Ch. 6.2-5) 1PP · 2PP 
4PP · 6PP
HW3 (optional)
Thu 7/3 Game Trees: Expectimax; Utilities Ch. 5.2-5 (2e: Ch. 6.2-5), 16.1-16.3 1PP · 2PP 
4PP · 6PP
P2: Multi-Agent Pacman
(section / solutions)
Mon 7/7

Mon 7/7 Markov Decision Processes Ch. 17.1-3 1PP · 2PP 
4PP · 6PP
HW4 Wed 7/9
Tue 7/8 Markov Decision Processes II Ch. 17.1-3, Sutton and Barto Ch. 3-4 1PP · 2PP 
4PP · 6PP
(section / solutions)
Wed 7/9 Reinforcement Learning Ch. 21, S&B Ch. 6.1,2,5 1PP · 2PP 
4PP · 6PP
P3: Reinforcement Learning
HW5
Mon 7/14
Fri 7/11
Thu 7/10 Reinforcement Learning II Ch. 21 1PP · 2PP 
4PP · 6PP
Practice Midterm
(section / solutions)
 Tue 7/15
 

Mon 7/14 Review    
 
   
Tue 7/15 No Lecture    
 
 
Wed 7/16 Midterm, (3-6 PM, 245 Li Ka Shing)    
 
   
Thu 7/17 Probability Ch. 13.1-5 (2e: Ch. 13.1-6) 1PP · 2PP 
4PP · 6PP
 

Mon 7/21 Bayes' Nets: Representation Ch. 14.1-2,4 1PP · 2PP 
4PP · 6PP
HW6 7/24
Tue 7/22 Bayes' Nets: Independence Ch. 14.1-2,4 1PP · 2PP 
4PP · 6PP
(section / solutions)  
Wed 7/23 Bayes' Nets: Inference Ch. 14.4, Jordan 2.1 1PP · 2PP 
4PP · 6PP
7/28
Thu 7/24 Bayes' Nets: Sampling Ch. 14.4-5 1PP · 2PP 
4PP · 6PP
HW7
(section / solutions)
 

Mon 7/28 Decision Diagrams / VPI Ch. 16.5-6 1PP · 2PP 
4PP · 6PP
HW8 7/31
Tue 7/29 Markov Models Ch. 15.2,5 1PP · 2PP 
4PP · 6PP
(section / solutions)  
Wed 7/30 Hidden Markov Models Ch. 15.2,5 1PP · 2PP 
4PP · 6PP
P4: Ghostbusters 8/5
Thu 7/31 Applications of HMMs Ch. 15.2,6 1PP · 2PP 
4PP · 6PP
(section / solutions)  

Mon 8/4 ML: Naive Bayes Ch. 20.1-20.2.2 1PP · 2PP 
4PP · 6PP
   
Tue 8/5 ML: Perceptrons Ch. 18.6.3 1PP · 2PP 
4PP · 6PP
Final Contest 
(section / solutions)
8/11
Wed 8/6 ML: Kernels and Clustering Ch. 18.8 1PP · 2PP 
4PP · 6PP
HW9 8/11
Thu 8/7 Advanced Applications: (Robotics and Computer Vision)   1PP · 2PP 
4PP · 6PP
Practice Final 
(section / solutions)
8/12

Mon 8/11 Advanced Applications: NLP, Games and Cars   1PP · 2PP
4PP · 6PP
   
Tue 8/12 Review   1PP · 2PP 
4PP · 6PP
 
Wed 8/13 Advanced Topics and Final Contest   1PP · 2PP
4PP · 6PP
   
Thu 8/14 Final Exam, (3-6 PM, 245 Li Ka Shing)