EEL6825: Course Outline

EEL 6825 COURSE OUTLINE

AUG 27 L01 Intro, Bayes Rule
       L02 Bayes Decision Rule, 1D example, two-class, multi-class,
           minimum cost
AUG 29 L03 Univariate Normal, multivariate, covariance matrix,
           linear transforms, eigenvectors/values, plotting

SEP 03 L04 Mahalanobis distance, linear transform of,
           what do discrim surfaces look like? sigma_i same, different
       L05 review quadratic decision boundaries
SEP 05 L06 HW#1 DUE
           compute Bayes error for multivariate (in general hard)
           how does error change with dimension?
           normals are not the only distrib (exponential example) 
SEP 10 L07 Maximum Liklihood estimates of mean and variance
           bias, consistency
           how to create arbitrary Gaussian distribs
       L08 review linear transforms, distance is preserved
           whitening, distance is not preserved
           simultaneous diagonalization
SEP 12 L09 Bhat bound
           Parametric classifiers, linear classifiers
           linear classifier design
           Fisher criterion
SEP 17 L10 linear classifiers/more interpretation
           linear separable
           MSE, iterative schemes
       L11 MSE scheme
           resubstitution/holdout method
           test vs. training set
 
SEP 19 L12 HW#2 DUE
           nonlinear features
           quadratic classifier

SEP 24 L13 generalization of linear bayes to multiclass,
            curse of dim
           bad use of test and training
       L14 How much data do you need?
           how much data for classifier, for estimating error?
SEP 26 L15 Bayes estimation

OCT 01 L16 1-NN
       L17 
OCT 03 L18 k-NN

OCT 08 L19 HW#3 DUE, go over HW
       L20 Review for Exam
OCT 10 L21 EXAM I  (part 1)
           

OCT 15 L22 EXAM I  (part 2)
       L23 Go Over Exam
OCT 17 L24 KL transform theory

OCT 22 L25 KL Transform
       L26 individual meetings with students
OCT 24 L27 KL transform

OCT 29 L28 KL examples/problems
       L29 Introduction to speech processing
OCT 31 L30 LPC and filter-bank coding for speech

NOV 05 L31 Dimensionality reduction for classification
       L32 Misc finish speech
NOV 07 L33 Dimensionality reduction      HW#4 due

NOV 12 L34 Dimensionality reduction
       L35 Image processing/Connected components 
NOV 14 L36 Image processing/Feature extraction

NOV 19 L37 Go over projects
       L38 Computer Vision
NOV 21 L39 Clustering

NOV 26 L40 HW#5 due, trace optimization
       L41 Review for Exam 2
NOV 28 THANKSGIVING

DEC 03 L42 Exam 2
       L43 
DEC 05 L44 Guest Lecture: Mr. Xu

DEC 10 L45 Go over exam, Final projects due
       L46 Selected Presentations