Instructor:
Dr. Ahmed Elgammal -- email: elgammal at cs…
Class Time: Monday 6:40-9:30 pm Hill 254
Office hours: Tuesday 4:30-6:30pm
TA: Nishkam Ravi Email: nravi at cs…
TA office hours: Tuesday 4-6pm Core 334
Class Web page: http://www.cs.rutgers.edu/~elgammal/cs536.html
Class Calendar and Lecture Slides
Overview:
This is a basic graduate-level Machine Learning class that intends to cover a variety of fundamental Machine Learning topics to get you acquainted with the field and it’s applications.
This is not a comprehensive list of topics nor it reflects the order we will present the topics. It’s more a highlevel abstraction of the topics:
Basics: What’s machine learning, dealing with data, Concept Learning, Version Spaces.
Linear Models: Linear
Regression, Naïve Bayes
Nonlinear Models: Neural
Networks, Instant Based Learning, Decision Trees, Boosting, MDL.
Margin-based Approaches:
Support Vector Machines and Kernels Methods
Learning theory: VC
dimension, PAC learning, Error Bounds
Unsupervised Learning:
Clustering, K-means, Expectation Maximization, dimensionality Reduction.
Structured Models: Graphical
Models, Hidden Markov Models.
Reinforcement Learning and Evolutionary Learning.
Linear algebra and basic
probability and statistics.
General Machine Learning
text books:
Introduction to Machine
Learning, Ethem Alpaydin- MIT press 2004
Machine Learning, Tom Mitchell. 1997
Pattern Recognition and
Machine Learning, Christopher M.
Bishop, Springer 2006
Pattern
Classification (2nd Edition), Duda, Hart and Stork., Wiley 2001
Topic-Oriented text books:
Learning With Kernels,
Bernhard Scholkopf and Alexaner J. Smola
An Introduction To Genetic
Algorithms, Melanie Mitchell
Neural Networks for Pattern
Recognition, Chris Bishop.
Principle of Data Mining,
Hand Mannila, and Smyth
Class Project:
Students
(in groups of 2 or indviduals) are expected to work on a class research project
throughout the semester to explore a recent Machine Learning research topic.
Students are to choose their own projects and are encouraged to find a project
related to their own research. The project ideas are expected to be innovative,
experimental and feasible to be done within the semester time frames.
Project
time line (tentative- to be modified)