[ICS 435] Machine Learning Fundamentals
Date 
Activities 
Homework etc. 
8/25/17 
Introduction. 
First HW given. (Perceptron) 
9/1 
Remarks on presenting
scientific
results. 
First HW due. 
9/8 
Supervised learning I: Generalization Error vs. training error. Introduction to statistical learning theory and support vector machines (SVM)  
9/15 
Support vector learning: Support
vector
machines
(SVM) and Support vector regression (SVR).

Second HW given.
(SVM/R) 
9/22 
Introduction to regression
and Bayesian Inference. 
Extra credit HW
given.
(Bayesian Interpolation) 
9/29 
Guest lecture: Prasad
Santhanam on linear regression. 

10/6 
Supervised
learning II: Introduction to artificial neural networks and
deep learning. 

10/13 
Guest tutorial: Giacomo
Indiveri on Neuromorphic engineering. 
Second
HW due. 
10/20 
Introduction to unsupervised
learning. 
Third HW given. (kmeans) 
10/27 
Introduction
to the use of information theory in unsupervised learning. 

11/3 
From thermodynamics to information theory to machine learning.  
11/10 
Behavioral learning (pending student interest and time frame). OR work on Final Project  Third HW due. EC HW due. 
11/17 
Work on Final Project.  
11/24 
Thanksgiving break. 

12/1 
Final Project Presentations.  Final Project due. 
12/8 
Study period. Last day of
instruction is 12/7. Final taken online the following week. 
Final Exam. 