CS690: Machine Learning
Fall 2009
Time and Location: Tue, Thu 10:10am &ndash 12:00pm, Convocation Center 176
Instructor: Razvan Bunescu
Office: Stocker 337
Office Hours: Mon, Thu 10am &ndash 12pm, or by email appointment
Email: bunescu @ ohio edu
Textbook:
Pattern Recognition and Machine Learning by Christopher Bishop. Springer, 2007.
Recommended Supplementary Text:
Machine Learning by Tom Mitchell. McGraw Hill, 1997
Course description:
Machine Learning is concerned with the design and analysis of
algorithms that enable computers to automatically find patterns in the
data. This introductory course will give an overview of the main
concepts, techniques and algorithms that are relevant for the theory
and practice of machine learning. The course will cover the
fundamental topics of classification, regression and clustering,
starting with simple learning models such as perceptrons, decision
trees and logistic regression, and ending with more advanced models
including Support Vector Machines, Conditional Random Fields and
Bayesian networks. The description of the formal properties of the
algorithms will be supplemented with motivating applications in a wide
range of areas including natural language processing, computer vision,
bioinformatics and music analysis.
Prerequisites:
The students are expected to be comfortable with programming in at
least one of C/C++/Java, and to exhibit a basic level of mathematical
dexterity. Relevant background material in linear algebra, probability
theory and information theory will be made available during the
course.
Lecture notes: