(the schedule may change to adjust to the actual pace of the class)

Academic add/drop dates

ITILA: Information Theory, Interence and Learning Algorithms
AIMA: Artificial Intelligence a Modern Approach
WP: Wikipedia entry

Lecture slides
(filename format: NN-topic.pdf or NN-MM-topic.pdf, where NN or NN-MM is the approximately associated class or range of classes)
Slides may change up to day and time of the lecture. Assignments may change up to the release day.
How to print multiple slides per page

Class Date Topic Reading Assignments / Comments
1 Jan 4 What is AI? AI in practice AIMA, ch. 1,2  
2 Jan 9 Probability review ITILA, ch. 2, WP ass. 1 out
3 Jan 11 Probability review - Entropy ITILA, ch. 2, WP  
4 Jan 16 Probability review - Entropy ITILA, ch. 8, 23 WP  
5 Jan 18 Information theory ITILA, ch. 1, WP  
6 Jan 23

Information theory
Probabilistic inference

ITILA, ch. 3, 4, 5, 6 WP, WP  
7 Jan 25 Uncertainty AIMA, ch. 13 WP  
8 Jan 30 Uncertainty AIMA, ch. 13 WP ass. 1 due,
ass. 2 out
9 Feb 1 Uncertainty AIMA, ch. 13 WP  
10 Feb 6 Probabilistic reasoning AIMA, ch. 14a WP WP WP  
11 Feb 8 Probabilistic reasoning AIMA, ch. 14b  
12 Feb 13 Probabilistic reasoning AIMA, ch. 14b  
13 Feb 15 Probabilistic reasoning over time,
Markov processes
 AIMA, ch. 15a WP (M. Shafiei)
  Feb 19-23 Study break    
14 Feb 28 Probabilistic reasoning over time,
Inference tasks
 AIMA, ch. 15a  
15 Mar 1 Viterbi algorithm AIMA, ch. 15a WP ass2. due, ass. 3 out
16 Mar 6 Hidden Markov models AIMA, ch. 15a WP  
17 Mar 8 Kalman filters covariance 1 2 WP
multivariate Gaussian 1 2 3 4 		 
18 Mar 13 Dynamic Bayesian nets AIMA, ch. 15a project proposal due
(max. 1 page)
19 Mar 15 Particle filters WP  
20 Mar 20 Speech recognition AIMA, ch. 15b WP ass. 3 due
21 Mar 22 Speech recognition AIMA, ch. 15b  
22 Mar 27 review/Q&A    
23 Mar 29      
24 Apr 3 Exam    
25 Apr 5      
         
26        
  Apr 22 Project due date    
         
    Other interesting topics for self-study  

 
    Decision making AIMA, ch. 16,17 WP  
    Statistical learning (neural nets, bayesian nets) ch. 20  
    Reinforcement learning ch. 21  
    Natural language processing (statistical) ch. 23  
    Natural language processing (linguistic) ch. 22  
    Machine Perception ch. 24  
    Robotics ch. 25