CSC535: Probabilistic Graphical Models Page 1 CSC380: Principles of Data Science
Introduction to Machine Learning Prof. Jason Pacheco TA: Enfa Rose George TA: Saiful Islam
Salim With content from Prof. Kwang Sung-Jun Page 2 What is machine learning? • Tom
Mitchell established Machine Learning Department at CMU (2006). • A bit outdated with recent
trends, but still has interesting discussion (and easy to read). • A subfield of Artificial Intelligence
– you want to perform nontrivial, smart tasks. The difference from the traditional AI is “how” you …

Abstract Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally
in controlled Markovian domains. It amounts to an incremental method for dynamic
programming which imposes limited computational demands. It works by successively
improving its evaluations of the quality of particular actions at particular states. This paper
presents and proves in detail a convergence theorem for Q-learning based on that outlined
in Watkins (1989). We show that Q-learning converges to the optimum action-values with …

The automated categorization (or classification) of texts into predefined categories has
witnessed a booming interest in the last 10 years, due to the increased availability of
documents in digital form and the ensuing need to organize them. In the research
community the dominant approach to this problem is based on machine learning
techniques: a general inductive process automatically builds a classifier by learning, from a
set of preclassified documents, the characteristics of the categories. The advantages of this …

A comprehensive introduction to machine learning that uses probabilistic models and
inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for
automated methods of data analysis. Machine learning provides these, developing methods
that can automatically detect patterns in data and then use the uncovered patterns to predict
future data. This textbook offers a comprehensive and self-contained introduction to the field
of machine learning, based on a unified, probabilistic approach. The coverage combines …

Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today's most rapidly growing technical fields,
lying at the intersection of computer science and statistics, and at the core of artificial
intelligence and data science. Recent progress in machine learning has been driven both by
the development of new learning algorithms and theory and by the ongoing explosion in the
availability of online data and low-cost computation. The adoption of data-intensive machine …