Introduction to Machine Learning; Classification of learning: unsupervised and supervised learning, connectionist learning, reinforcement learning, machine discovery; Supervised learning: Information theoretic decision tree learner, best current hypothesis search, candidate elimination (version space) algorithm, learning in the first order Horn clause representation, inductive logic programming, applications; Unsupervised learning: hierarchical clustering, category utility, incremental and non-incremental algorithms for hierarchical clustering, applications; Connectionist learning: introduction to neural networks, Feed forward and recurrent networks, perception, multilayer feed forward networks, backpropagation algorithm for training a feed forward network, applications; Genetic algorithms: genetic operators, fitness function, genetic algorithm in supervised learning framework, applications.

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• Be able to articulate key concepts and principles in Machine learning.
• Be able to articulate and model problems given an understating of representational issues and abstraction in machine learning.
• Be able to explain and analyze models and results making use of theoretical principles and the limitations of generalization in machine learning.
• Make use of the algorithmic theory of machine learning in problem analysis and model selection.
• Understand and apply the maximum likelihood principle and explain algorithmic implications in modeling and problem solving.
• Be able to use a variety of algorithmic techniques in machine learning.
• Be able to choose and use a variety of machine learning protocols in different situations.

1. Introduction to Machine Learning by Ethem Alpaydin
2. Pattern Recognition and Machine Learning by Chris Bishop
3. Machine Learning: a Probabilistic Perspective by Kevin Murphy