Computer Science Tutorials

MACHINE LEARNING OBJECTIVES: Familiarity with a set of well-known supervised, unsupervised and semi-supervise   Learning algorithms.   The ability to implement some basic machine learning algorithm. Understanding of how machine learning algorithms are evaluated. OUTCOMES: Recognize the characteristics of machine learning that make it useful to real-world Problems. Characterize machine-learning algorithms as supervised, semi-supervised, and unsupervised. Have heard of a few machine learning toolboxes. Be able to use support vector machines. Be able to use regularized regression algorithms. Understand the concept behind neural networks for learning non-linear functions.

MACHINE LEARNING UNIT -I: The ingredients of machine learning, Tasks: the problems that can be solved with machine learning, Models: the output of machine learning, Features, the workhorses of machine learning. Binary classification and related tasks: Classification, Scoring and ranking, Class probability estimation UNIT-II: Beyond binary classification:  Handling more than two classes, Regression, Unsupervised and descriptive learning. Concept learning : The hypothesis space, Paths through the hypothesis space, Beyond conjunctive concepts UNIT- III: Tree models: Decision trees, Ranking and probability estimation trees, Tree learning as variance reduction. Rule models: Learning ordered rule lists, Learning unordered rule sets, Descriptive rule learning, First-order rule learning UNIT-IV: Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear cla...