Course Outline (Video-Taped Session)

Week	Text & notes	Topics	Homework & project
1	Chap 1	lec 1 Overview, lec 2 Applications
2	Chap 1, 2.5, 2.8-2.13	lec 3 ANN basics, lec 4-5 Learning (1), (2) learning theory, competitive learning
3	2.4, 8.1-8.7, 2.2, 3.1-3.9	lec 6-8 learning (3), (4), (5) Hebbian learning, principal component network, error correcting learning, LMS, perceptron learning
4	4.1-4.5	lec 9-11 Multilayer perceptron (1)-(3): feedforward model, back-propagation training	HW#1 due
5	4.6-4.17	lec 12-14 Multilayer perceptron (3)-(6): momentum, initialization, learning rate adaptation, Generalization, cross-validation, bootstrapping, Structural design
6	3.10, 5.2, notes	lec 15-16 Classification. lec 17 SVM (1)
7	6.2-6.6	lec 18-19 SVM (2), (3), lec 20 clustering (1)	HW#2, Project proposal due
8	9.2 - 9.9,	lec 21-23 clustering (2)-(4), Self-Organization Map (SOM), Learning Vector Quantization,
9	5.3 - 5.10, notes	lec 24-25 Radial Basis Network (1), (2); lec 26 time series prediction,
10	Notes	lec 27system identification, lec 28 control and expert system. lec 29 fuzzy systems (1)	HW#3 due
11	Notes	lec 30-32 fuzzy systems (2)-(4)
12	Notes	lec 33 Fuzzy Iogic control (1)
13	Notes	lec 34, 35 Fuzzy Iogic control (2), (3), lec 36 Genetic Algorithms (1)	HW#4 due
14	Notes	lec 37 Genetic Algorithms (2), lec 38 Mixture of experts, lec 39 review and summary
15	Notes	Final Project Presentation	Takehome final: TBA
TBA		Take home final examination AND project report due TBA