Three 50-minutes lectures per week (3-0-3)
Designation: Elective Course
Course Level: Undergraduate
Prerequisite(s) by Topic:
- Basics of logic and counting
- Discrete probability
- Fundamental programming constructs
- Algorithms and problem-solving
- Fundamental data structures
- Basic algorithmic analysis
- Fundamental issues in intelligent systems
- Search and constraint satisfaction
- Knowledge representation and reasoning
Introduction to machine learning; Concept learning; Supervised learning - decision tree learning; Unsupervised learning - clustering. Artificial neural networks. Evaluating hypotheses; Bayesian learning; Computational learning theory; Instance based learning. Genetic algorithms; Learning sets of rules - Inductive Logic Programming; Reinforcement learning; Analytical learning;
Machine Learning, Tom Mitchell, McGraw Hill, 1997.
Reference(s) and Other Material:
- Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
- Element of Machine Learning, Pat Langley, Morgan Kaufmann Publishers, 1995.
After completion of this course, the student shall be able to:
- Understand the definition, theory and methodology of inductive learning.
- Understand the definition and some techniques of clustering.
- Understand the basics and some learning aspect of neural networks.
- Understand the basics and some learning aspect of genetic algorithms.
- Understand the basics of reinforcement learning.
- Understand the basics of explanation based learning.
- Practice the application of existing, as well as more recent machine learning approaches to real-world data, and evaluate/compare these approached.
- Concept Learning
- Decision Tree Learning
- Artificial Neural Networks
- Evaluating Hypotheses, Over fitting
- Bayesian Learning
- Instance Based Learning
- Computational Learning Theory
- Bayesian (Belief) Networks
- Genetic Algorithms
- Learning Sets of Rules
- Reinforcement Learning
- Analytical Learning