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
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:
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
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)