EECS 700 Machine Learning

Instructor	Text Book	Grades
Course Description	Exam	Lectures
Prerequisites	Project	Useful Links

Instructor

Dr. Xue-wen Chen

Ø Phone: (785)864-8825

Ø Email: xwchen AT ku DOT edu

Ø Office: Eaton 2028

Ø Lectures: TR, 4:00 pm – 5:15 pm

Ø Location: Room 2111 Learned Hall

Ø Class Number: 27530

Ø Office Hours: TR: 1:30 pm – 3:30 pm or by appointment

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Course Description

“Machine learning is the study of computer algorithms that improve automatically through experience” (Tom Mitchell). This course introduces basic concepts and algorithms in machine learning. A variety of topics such as Bayesian decision theory, dimensionality reduction, clustering, multilayer perceptrons, hidden Markov models, decision trees, support vector machines, combining multiple learners, reinforcement learning, Bayesian learning etc. will be covered (prerequisites: EECS 461 or equivalent or consent of instructor).

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Prerequisites

Ø Mathematical maturity, e.g., knowing to use algebra in solving problems.

Ø EECS 461 or equivalent or consent of instructor.

Ø Some experience with computer programming (MATLAB or C or C++).

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Text Book

Ø Tom Mitchell. Machine Learning, 1997. ISBN 0-07-042807-7, WCB/McGraw-Hill.

Ø Ethem Alpaydin. Introduction to Machine Learning, 2004. ISBN: 0-262-01211-1, the MIT Press.

Ø Additional handouts will be provided in class as needed.

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Exam

Ø No exam.

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Project

Students will complete several programming assignments and a final course project. The course project will be conducted on some machine learning competition datasets as announced in class.

Each student will give classroom presentation about the final project.

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Grades

Course grade will be assigned based on scores on exam, homework assignments, final project, and presentation. The final grade is made up of the following:

Ø Assignments: 40%

Ø Final Project: 50%

Ø Presentation 10%

The cutoffs for grades will be roughly as follows:

A: 90 – 100 B: 80 – 89 C: 70 – 79 D: 60 – 69 F: 0 – 59

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Lectures (slides are linked)

Ø Introduction: what is machine learning, classification, regression, unsupervised learning, reinforcement learning

Ø Supervised Learning

Ø Bayesian Decision Theory

o Loses and Risks

o Discriminant Functions

o Bayes Estimator

o Bayesian Networks

Ø Parametric Methods

o ML Estimation

o MAP Estimation

o Bayes Estimation

Ø Multivariate Methods

Ø Dimensionality Reduction

o Feature Selection

o Principal Components Analysis

o Factor Analysis

o Multidimensional Scaling

o Linear Discriminant Analysis

Ø Unsupervised Learning

o Mixture Densities

o k-means

o E-M Algorithm

o Hierarchical Clustering

o Spectral Clustering

Ø Supervised Learning

o Decision Trees

o Multilayer Perceptrons

o Bayes Learning

o SVMs etc.

Ø Combining Multiple Learners

o Voting

o ECOC

o Bagging

o Boosting etc.

Ø Reinforcement Learning

o Model-based Learning

o Temporal Difference Learning etc

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Useful Links

Ø Kernel Machines (software, tutorial, papers etc. about kernel machines)

Ø WEKA (Machine learning software in Java)

Ø UTEXAS Software (Machine learning research software)

Ø CMU Software (Machine learning software)

Ø UCI REPOSITORY (Machine learning datasets)

Ø Conferences (Machine learning conference information)

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