Concepts and algorithms of machine learning including version-spaces, decision trees, instance-based learning, networks, evolutionary computation, Bayesian learning and reinforcement learning.

A course in data structures (such as CSE 274)

- 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.

- 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)

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