Course description:
The course provides an introduction to machine learning. The course will provide an
overview to machine learning ranging from statistical-based method, kernel based
method, to contemporary deep learning method. It also includes hands-on exercises.
Textbook:
Introduction to Machine Learning, Fourth Edition, Ethem Alpaydin
Teaching Method:
Lecture
Syllabus (Tentative):
Section 1
Introduction to Machine Learning
Supervised Learning
Bayesian Decision Theory
Parametric Model
Multivariate Methods
Section 2
Clustering/Dimension Reduction
Decision tree
Linear Discrimination
Support Vector Machine
Section 3
Multilayer Perceptron
Deep learning
Design and Analysis of Machine Learning Experiment
Invited Lecture
Nvidia DLI Fundamental of deep learning – Hands on lecture (深度學習系列實作坊: 深度學
習實作與理論)
Invited Lecture 1/10
Grading (Tentative):
3 Homework (13% each)
1 Midterm (20%)
1 Final (35%)
Hand-on exercise (6%)
Website:
We will use eelearn
The first day of class (Sept 13)
https://teams.microsoft.com/l/meetup-
join/19%3ameeting_ZWQ1M2FmOTctOTExMi00MzkwLTg2NDktMTlkM2E4NjkwMzkx%40thread.v2/0?
context=%7b%22Tid%22%3a%226c3bc511-43c7-4596-baeb-
2335c69c41f1%22%2c%22Oid%22%3a%22c6846cb1-b32f-45b5-8962-cf02f104b335%22%7d