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V22-0480-006, Spring 2005:
Introduction to Machine Learning and Pattern Recognition

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

Introductory course on machine learning, pattern recognition, neural nets, statistical modeling for undergraduates.

Instructor: Yann LeCun, 715 Broadway, Room 1220, x83283, yann [ a t ] cs.nyu.edu (note: new room number).

Teaching Assistant: Raia Hadsell, 715 Broadway, 10th floor, raia [ a t ] cs.nyu.edu.

Classes: Tuesdays and Thursday, 3:30-4:45PM, Room 102, Warren Weaver Hall.

Computer Lab: Room 512, Warren Weaver Hall.

Office Hours for Prof. LeCun: Wednesdays 2:00-4:00 PM

Office Hours for Raia: TBA

Click here for schedule and course material >>>

Course Description

This course will cover a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. The course will cover the mathematical methods, theoretical aspects, algorithms, implementation issues, and applications.

Machine Learning and Pattern Recognition methods are at the core of many recent advances in "intelligent computing". Current applications include machine perception (vision, audition, speech recognition), control (process control, robotics), data mining, time-series prediction (e.g. in finance), natural language processing, text mining and text classification, bio-informatics, neural modeling, computational models of biological processes, and many other areas.

Who Can Take This Course?

This course can be useful to all students who would want to use or develop 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).

The only formal pre-requisites are familiarity with computer programming and linear algebra, but the course relies heavily on such mathematical tools as probability and statistics, multi-variate calculus, and function optimization. The basic mathematical concepts will be introduced when needed, but the students will be expected to assimilate a non-trivial amount of new mathematical concepts in a fairly short time.

Therefore, the course is primarily for students at the senior and junior levels.

Topics Treated

The topics studied in the course include:
  • the basics of inductive inference, learning, and generalization.
  • linear classifiers: Perceptron, LMS, logistic regression.
  • non-linear classifiers with linear parameterizations: basis-function methods, boosting, Support Vector Machines.
  • multilayer neural networks, backpropagation
  • heterogeneous learning systems
  • Energy-based models, loss functions
  • optimization methods in learning: gradient-based methods, second-order methods.
  • Probabilistic models, Bayesian methods, the Expectation-Maximization algorithm.
  • graph-based models for sequences: Hidden Markov Models, finite-state transducers, recurrent networks.
  • unsupervised learning: density estimation, clustering, and dimensionality reduction methods.
  • introduction to graphical models and factor graphs
  • approximate inference, sampling.
  • Learning theory, the bias-variance dilemma, regularization, model selection.
  • applications in computer vision, handwriting recognition, speech recognition, robotics, natural language processing, financial forecasting, biological modeling...
By the end of the course, students will be able to understand, implement, and apply all the major machine learning methods.

This course will be a adapted version of the graduate course G22-3033-002 taught in Fall 2004. Please visit the site of the Fall 2004 Graduate-level version of this course to have a look at the schedule and source material.


The best way (some would say the only way) to understand an algorithm is to implement it and apply it. Building working systems is also a lot more fun, more creative, and more relevant than pen-and-paper problems.

Therefore students will be evaluated primarily on programming projects given on a 2 week cycle. There will also be a final exam and a final project.


Linear algebra and good programming skills are the only formal pre-requisites, but the course will use such mathematical concepts as vector calculus (multi-dimensional surfaces, gradients, Jacobians), elementary statistics and probability theory (Bayes rule, multi-variate Gaussian).

Good programming ability is a must: many assignements will consist in implementing algorithms studied in class. For most programming assignement, a skeleton code in the Lush language will be provided. Lush is an interpreted language that makes it easy to quickly implement numerical algorithms.

The course will include a short tutorial on Lush.

Lush can be downloaded and installed on Linux, Mac, and Windows (under Cygwin). See Chris Poultney's notes on installing Lush under Cygwin.

Lush is available on the CIMS Sun workstations.

Programming projects may be implemented in any language, (C, C++, Java, Matlab, Lisp, Python,...) but the use of a high-level interpreted language with good numerical support and and good support for vector/matrix algebra is highly recommended (Lush, Matlab, Octave...). Some assignments require the use of an object-oriented language.

Mailing List

Register to the course's mailing list.

Text Books

Richard O. Duda, Peter E. Hart, David G. Stork: "Pattern Classification" Wiley-Interscience; 2nd edition, October 2000.

I will not follow this book very closely. In particular, much of the material covered in the second half of the course cannot be found in the above book. I will refer to research papers and lectures notes for those topics.

Either one of the following books is also recommended, but not absolutely required (you can get a copy from the library):

Other Books of Interest

  • S. Haykin: "Neural Networks, a comprehensive foundation", Prentice Hall, 1999 (second edition).
  • Tom Mitchell: "Machine Learning", McGraw Hill, 1997.

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 opportunities for 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 students in the class (WWH Room 512). See Chris Poultney's notes on installing Lush under Cygwin.
  • Torch: A C++ library for machine learning.


Some of those papers are available in the DjVu format. The viewer/plugins for Windows, Linux, Mac, and various Unix flavors are available here.

Many papers are available from Prof. LeCun's publication's page.

Here are a few paper that are relevant to the course.

  • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. [PS.GZ] [DjVu]
  • Y. LeCun, L. Bottou, G. Orr, and K. Muller, "Efficient BackProp," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.), 1998. [PS.GZ] [DjVu]
  • P. Simard, Y. LeCun, J. Denker, and B. Victorri, "Transformation Invariance in Pattern Recognition, Tangent Distance and Tangent Propagation," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.), 1998. [PS.GZ] [DjVu]

Publications, Journals

Conference Sites


Demos and Pretty Pictures

Demo of Convolutional Nets for handwriting recognition.

More demos are available here.

Object Recognition with Convolutional Neural Nets