Advanced Machine Learning


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Course Information January, 2015

Day & Time and Location

M/W 1:10-2:25 at TBD


Professor Tony Jebara

Office Hours

Mon 2:30-3:30 in CEPSR 605

Prerequisites: COMS W4771 or permission. Background in linear algebra and statistics.

Description: Advanced machine learning tools with applications in perception and behavior modeling. Topics include Eigenspace Methods, Independent Components Analysis, Latent Models, Bayesian Inference, Kalman Filtering, Time Series Prediction, Hidden Markov Models, Dynamic Bayesian Networks, Markov Random Fields, Variational Methods, Support Vector Machines, Kernel Methods, Maximum Entropy, Transduction, Feature Selection, Meta-Learning, Dimensionality Reduction, Manifold Learning, and Spectral Clustering.

Click on "Handouts" for more details about what the course will cover. If you have not taken 4771 and want to take Advanced Machine Learning, we may make an exception for you if you have a strong background and are eager to catch up. To brush up on background material for Advanced Machine Learning, look at the slides and handouts for introductory Machine Learning COMS4771.

Bulletin Board: Class bulletin board (Click on Discussion)

Online Text Book: Introduction to Graphical Models The book is available via courseworks. Please login using your CUNI email address (for example and your email password. Registered students only.

Academic Honesty Policy: Please read the policy here. By staying registered in the class you indicate your acceptance of all its terms. We do not accept late homework or absence without official reasons (medical, etc.) approved by a student dean. If you miss class, please coordinate with colleagues to find out what you missed (do not email the professor to help you catch up). Once a particular grade is posted for you on Courseworks, you have two weeks to contest it. Afterwards, these grades cannot be changed (do not wait until the end of the semester to contest any grading issues that are more than two weeks old). This course assumes you have the ability to upload your work via courseworks and can figure out how to attach files. If you are incapable of using courseworks, unable to program, or unable to follow mathematical notation, please drop the class. If you find any of these terms unacceptable, please drop the class.

This 3 credit course (4772 or 6772 are equivalent) satisfies:
a PhD Elective in the Advanced AI track.
a Required or Elective Course for the MS Machine Learning track.
an Elective Course for the MS Vision/Graphics track.

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