|
Lecture #
|
Topic
|
Lecture slides
|
|
1
|
Course introduction
|
PDF
|
|
2
|
Linear Algebra, MATLAB®
|
PDF
|
|
3
|
Random variables, Statistics
|
PDF
|
|
4
|
Bayesian Decision Theory
|
PDF
|
|
5
|
Principal Components Analysis
|
PDF
|
|
6
|
Linear discriminant Analysis
|
PDF
|
|
7
|
Linear and Quadratic Classifiers
|
PDF
|
|
8
|
The K Nearest Neighbors Classifier
|
PDF
|
|
9
|
Parameter estimation, histogram, KNN
|
PDF
|
|
10
|
Kernel density Estimation
|
PDF
|
|
11
|
Multilayer Perceptrons: learning
|
PDF
|
|
12
|
Multilayer Perceptrons for classification
|
PDF
|
|
13
|
Validation
|
PDF
|
|
14
|
Statistical clustering
|
PDF
|
|
15
|
Competitive learning, Kohonen SOMs
|
PDF
|
|
16
|
Feature selection: sequential
|
PDF
|
|
17
|
Feature selection: exponential and
randomized
|
PDF
|