Catalog description:

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)