Course Materials

Date Lecture topics Suggested Readings Additional Materials
1 Sep 2015 Introduction: Representing sounds and images [slides]    
3 Sep 2015 Fundamentals of Linear Algebra I [slides]    
8 Sep 2015 Fundamentals of Linear Algebra II [slides]    
10 Sep 2015 Representing Signals: Images and Sounds [slides]    
15 Set 2015 Data-driven representations: Eigen features [slides]    
17 Sep 2015 Data-driven representations: Eigen features Continue    
22 Sep 2015 Adaboost and Face detection [slides]    
24 Sep 2015 Independent Component Analysis [slides]    
29 Sep 2015 Nonnegative Matrix Factorization [slides]    
1 Oct 2015 Clustering [slides]    
6 Oct 2015 Guest Lec 1: Data from Civil Infr.: The Good, The Bad and The Ugly Mario Berges  
8 Oct 2015 Guest Lec 2: Large Scale Multimedia Retrieval and Event Detection [slides] Gerald Friedland  
13 Oct 2015 Guest Lec 3: Compressive Sensing: Motivation, Theory, Recovery [slides] Aswin Sankarnarayanan  
15 Oct 2015 Clustering Continue    
20 Oct 2015 Guest Lec 4: Music information processing [slides] Roger Dannenberg  
22 Oct 2015 Sparse and overcomplete representations [slides]    
27 Oct 2015 Expectation Maximization [slides]    
29 Oct 2015 Regression and Prediction [slides]    
3 Nov 2015 Linear Gaussian Model [slides]    
5 Nov 2015 Gest Lec 5: Acoustic Event Detection Jort F. Gemmeke  
10 Nov 2015 Linear Gaussian Model Continue    
12 Nov 2015 Factor Analysis [slides]    
17 Nov 2015 Class Canceled    
19 Nov 2015 Markov Process (no slides, blackboard)    
24 Nov 2015 Hidden Markov Model [slides]    
26 Nov 2015 Thanksgiving    
1 Dec 2015 Predicting and Estimation I (Kalman Filter) [slides]    
3 Dec 2015 Predicting and Estimation II (Extended Kalman Filter) [slides]