Machine learning is a subfield of artificial intelligence 
which is concerned with computer programs that can automatically
improve their capabilities and/or performance by acquiring 
(learning) experience. The field has been growing steadily and 
producing many impressive applications in numerous fields of 
science and technology, including autonomous vehicles and robots,
medical diagnosis, information retrieval and filtering tools and 
security risk detection.

The main objectives of this course are:
  -An in-depth introduction to machine learning theory and 
     methods
  -Exploration of research problems in machine learning and its
     applications which may lead to work on a project or a 
     dissertation.

The course is intended primarily for computer science and 
artificial intelligence graduate students. Graduate students 
from other departments who have a strong interest and sufficient
experience in artificial intelligence may also find the course 
interesting.

* Part I: Machine learning techniques
  Selected from inductive learning, decision trees, neural 
network approaches, evolutionary computation approaches and 
classifier systems, reinforcement learning, statistical and 
Bayesian learning, instance-based learning, explanation-based 
learning and computational learning theory.

* Part II: Machine learning application
  Selected from data mining, medical diagnosis, fraud detection,
pattern recognition and/or other contemporary applications.

Machine learning is a subfield of artificial intelligence 
which is concerned with computer programs that can automatically
improve their capabilities and/or performance by acquiring 
(learning) experience. The field has been growing steadily and 
producing many impressive applications in numerous fields of 
science and technology, including autonomous vehicles and robots,
medical diagnosis, information retrieval and filtering tools and 
security risk detection.

The main objectives of this course are:
  -An in-depth introduction to machine learning theory and 
     methods
  -Exploration of research problems in machine learning and its
     applications which may lead to work on a project or a 
     dissertation.

The course is intended primarily for computer science and 
artificial intelligence graduate students. Graduate students 
from other departments who have a strong interest and sufficient
experience in artificial intelligence may also find the course 
interesting.

* Part I: Machine learning techniques
  Selected from inductive learning, decision trees, neural 
network approaches, evolutionary computation approaches and 
classifier systems, reinforcement learning, statistical and 
Bayesian learning, instance-based learning, explanation-based 
learning and computational learning theory.

* Part II: Machine learning application
  Selected from data mining, medical diagnosis, fraud detection,
pattern recognition and/or other contemporary applications.