Sample Course Schedule (Spring 2014)

Below is a sample schedule, which was the UC Berkeley Spring 2014 course schedule (14 weeks).

The optional readings, unless explicitly specified, come from Artificial Intelligence: A Modern Approach, 3rd ed. by Stuart Russell (UC Berkeley) and Peter Norvig (Google).

The lecture videos for Spring 2014 can be found under the "Video" column here, and additionally, under the Lecture Videos tab along with lecture videos from past semesters.

Under the videos column, there are additional Step-By-Step videos which supplement the lecture's materials. See the list of Step-By-Step videos here.

The links to homework assignments only work when you are logged in to and are registered for this course. See here for more detailed instructions.

Day Topic Optional Reading Slides Videos Assignment Due
Tu 1/21 Introduction to AI Ch. 1 PPT Lecture P0: Tutorial 1/24 5pm
Th 1/23 Uninformed Search Ch. 3.1-4 (2e: Ch. 3) PPT Lecture

Tu 1/28 A* Search and Heuristics Ch. 3.5-6 (2e: Ch. 4.1-2) PPT Lecture
HW1: Search
section 0 (solutions)
section 1 (solutions)
Th 1/30 Constraint Satisfaction Problems I Ch. 6.1 (2e: Ch. 5.1) PPT Lecture P1: Search 2/7 5pm

Tu 2/4 CSPs II Ch. 6.2-5 (2e: Ch. 5.2-4) PPT Lecture HW2: CSPs
section 2 (solutions)
Th 2/6 Game Trees: Minimax Ch. 5.2-5 (2e: Ch. 6.2-5) PPT Lecture

Tu 2/11 Game Trees: Expectimax; Utilities Ch. 5.2-5 (2e: Ch. 6.2-5), 16.1-16.3 PPT Lecture HW3: Games
section 3 (solutions)
Th 2/13 Markov Decision Processes Ch. 17.1-3 PPT Lecture P2: Multi-Agent Pacman 2/21 5pm

Tu 2/18 Markov Decision Processes II Ch. 17.1-3, Sutton and Barto Ch. 3-4 PPT Lecture HW4: MDPs
section 4 (solutions)
Th 2/20 Reinforcement Learning Ch. 21, S&B Ch. 6.1,2,5 PPT Lecture    

Tu 2/25 Reinforcement Learning II  Ch. 21 PPT Lecture HW5: RL
section 5 (solutions)
          P3: Reinforcement Learning 3/7 5pm
Th 2/27 Probability Ch. 13.1-5 (2e: Ch. 13.1-6) PPT Lecture Practice Midterm (solutions) 3/8

Tu 3/4 Markov Models Ch. 15.2,5 PPT Lecture    
Th 3/6 Hidden Markov Models Ch. 15.2,5 PPT Lecture    

Mo 3/10 Midterm 1 Exam (solutions)       HW6: Probability, HMMs
section 6 (solutions)
Th 3/13 Applications of HMMs Ch. 15.2,6 PPT Lecture P4: Ghostbusters 3/21 5pm

Tu 3/18 Bayes' Nets: Representation Ch. 14.1-2,4 PPT Lecture HW7: Bayes' Nets: Representation, Independence
section 7 (solutions)
Th 3/20 Bayes' Nets: Independence Ch. 14.1-2,4 PPT Lecture

Tu 3/25 Spring Break          
Th 3/27 Spring Break          

Tu 4/1 Bayes' Nets: Inference Ch. 14.4 PPT Lecture
HW8: Bayes' Nets: Inference, Sampling
section 8 (solutions)
Th 4/3 Bayes' Nets: Sampling Ch. 14.4-5 PPT Lecture

Tu 4/8 Decision Diagrams / VPI Ch. 16.5-6 PPT Lecture HW9: Decision Diagrams, VPI, ML: Naive Bayes
section 9 (solutions)
          Practice Midterm 2 (solutions) 4/19
Th 4/10 ML: Naive Bayes Ch. 20.1-20.2.2 PPT Lecture
Contest: Pacman Capture the Flag 4/27

Tu 4/15 ML: Perceptrons Ch. 18.6.3 PPT Lecture
Th 4/17 ML: Kernels and Clustering Ch. 18.8 PPT Lecture    

Mo 4/21 Midterm 2 Exam (solutions)       HW10: ML: Perceptrons, Kernels
section 10 (solutions)
section 11 (solutions)
Th 4/24 Advanced Applications: NLP, Games and Cars   PPT Lecture P5: Classification 5/9 5pm

Tu 4/29 Advanced Applications: (Robotics and Computer Vision)   PPT Lecture    
Th 5/1 Advanced Topics and Final Contest   PPT Practice Final (solutions) 5/10

Th 5/15 Final Exam (solutions)