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G22-3033-013,Spring 2006:
Advanced Machine Learning

[ Course Homepage | Schedule and Course Material | Mailing List ]

Seminar-style Graduate Course on advanced topice in machine learning and statistical modeling.

Instructor: Yann LeCun, 715 Broadway, Room 1220, x83283, yann [ a t ] cs.nyu.edu

Classes: Wednesdays 2:00-3:50PM, Room 1221, 719 Broadway.

Office Hours for Prof. LeCun: by appointment

Click here for schedule and course material >>>

Course Description

The course will be a combination of seminar-style sessions reviewing key papers from the literature and tutorial lectures on advanced research topics. The topics covered in the course will include:
Energy-Based Models

Who Can Take This Course?

This course is primarily intended for PhD students whose research work is related to machine learning, and for advanced MSc students who intend to do thesis work on machine learning.

Prerequisite: some background in machine learning. Having taken either "Machine Learning and Pattern Recognition" or "Foundations of Machine Learning" is recommended, but not an absolute requirement. If you have not taken one of the above course, you must obtain authorization from the instructor before registering for credit. If you are one of the few lucky undergrads who have passed the MLPR class and want to take thins class, send email to the instructor.

Everyone is welcome to audit the classes.

This course can be useful to all students who need to develop new statistical modeling methods. This includes students in CS (AI, Vision, Graphics), Math (System Modeling), Neuroscience (Computational Neuroscience, Brain Imaging), Finance (Financial modeling and prediction), Psychology (Vision), Linguistics, Biology (Computational Biology, Genomics, Bio-informatics), and Medicine (Bio-Statistics, Epidemiology).

Topics Treated

The topics studied in the course will include two types of topics:
  • classic work in areas not treated by any of the existing machine learning courses
  • recent work on the most actively researched topics in machine learning over the last few years.

The LAGR project


Evaluation will be primarily based on class participation. Each week a different student will be asked to present a paper from the literature to the rest of the class.

Automatic Face Detection

Mailing List

Register to the course's mailing list.

Machine Learning Research at NYU

Please have a look at the research project page of the Computational and Biological Learning Lab for a few example of machine learning research at NYU.

There are numerous opportunities for independent studies and even undergraduate research projects. Contact Prof. LeCun for details.



  • Lush: A simple language for quick implementation of, and experimentation with, numerical algorithms (for Linux, Mac, and Windows/Cygwin). Many algorithms described in this course are implemented in the Lush library. Lush is available on the department's Sun machines that are freely accessible to NYU graduate students. See Chris Poultney's notes on installing Lush under Cygwin.
  • Torch: A C++ library for machine learning.

Lush is installed on the department's PCs. It will soon be available on the Sun network as well.

Publications, Journals

Conference Sites