Course desc<x>ription: 
<br>The course provides an introduction to machine learning. The course will provide an 
<br>overview to machine learning ranging from statistical-ba<x>sed method, kernel ba<x>sed 
<br>method, to contemporary deep learning method. It also includes hands-on exercises.
<br>
<br>Textbook:
<br>Introduction to Machine Learning, Fourth Edition, Ethem Alpaydin
<br>
<br>Teaching Method:
<br>Lecture
<br>
<br>Syllabus (Tentative):
<br>Section 1
<br>Introduction to Machine Learning
<br>Supervised Learning
<br>Bayesian Decision Theory
<br>Parametric Model
<br>Multivariate Methods
<br>
<br>Section 2
<br>Clustering/Dimension Reduction
<br>Decision tree
<br>Linear Discrimination
<br>Support Vector Machine
<br>
<br>Section 3
<br>Multil<x>ayer Perceptron
<br>Deep learning
<br>Design and Analysis of Machine Learning Experiment
<br>
<br>
<br>Grading (Tentative):
<br>3 Homework (13% each) 
<br>1 Midterm (20%)
<br>1 Final (35%)
<br>Hand-on exercise (6%)
<br>
<br>
<br>Website:
<br>We will use eelearn
<br>
<br>