Concepts and algorithms of machine learning including version-spaces, decision trees, instance-based learning, networks, evolutionary computation, Bayesian learning and reinforcement learning.
Prerequisite:
A course in data structures (such as CSE 274)
Course Objectives:
Describe and demonstrate the major paradigms of machine learning algorithms and knowledge structures.
Articulate the strengths and weaknesses, the appropriateness of various algorithms and the tradeoffs involved in machine learning implementations.
Implement software using one or more machine learning approaches.
Study and critique machine learning research literature.
Required topics (approximate weeks allocated):
Introduction to machine learning (0.5)
Concept learning (1.0)
Concept learning as search
Version spaces
Decision trees (2.0)
Representation
Decision tree learning algorithm
Inductive bias
Neural networks (2.0)
Perceptron learning rule
Multi-layered networks
Backpropagation learning algorithm
Bayesian Learning (1.5)
Bayes theorem
Bayes optimal classifier
Bayesian networks
Instance-based learning (1.5)
K-nearest neighbor classifier
Case-based reasoning
Radial basis functions
Evolutionary computation (1.5)
Genetic algorithms
Genetic programming
Reinforcement Learning (1.0)
Q Learning
Temporal Difference Learning
Applications and subtopics of machine learning -- examples are: (3.0)
Analytical Learning
Combining Inductive and Analytical Learning
Computational Learning Theory
Ensemble learning
Evaluating Hypotheses
Evolutionary programming and evolutionary strategies
Fuzzy Learning
Learning Sets of Rules
Machine learning applied to games
Unsupervised learning
Exams/Review (1)
Department of Computer Science and Software Engineering
