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Machine Learning and Pattern Recognition: Assignments
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Skeleton code for the assignments are provided in the Lush language.
You may use other languages to implement your assignments. but it is likely that the time you will spend re-implementing infrastructure code will be larger than the time to get familiar with Lush (except perhaps for the first assignment). For the adventurous among us, there is an open source C++ library called eblearn that implements all the functionalities of the Lush numerical library and machine learning library. It's still a bit rough, but usable. Lush is available for Linux and Mac OS here: http://lush.sf.net. If you only have a Windows machine, you have several solutions:
Installing Lush
Do not install the Lush package that comes with Ubuntu. It is badly out of date, rather, install Lush from the CVS repository on SourceForge as explained below. After you have installed Ubuntu Linux, you must install a number of packages using the shell commands below (type your password when prompted): sudo apt-get install gcc g++ libx11-dev binutils-dev indent libreadline5 libreadline5-dev sudo apt-get install emacs cvs libgsl0-dev imagemagick then, at the shell, do: cd cvs -d:pserver:anonymous@lush.cvs.sourceforge.net:/cvsroot/lush login [type enter when prompted for a password] cvs -z3 -d:pserver:anonymous@lush.cvs.sourceforge.net:/cvsroot/lush co lush cd lush ./configure makeThis will download and compile Lush in your home directory. The Lush executable is in lush/bin/i686-pc-linux-gnu/lush. For convenience, you can link this executable to some place in your shell search path, for example with: cd mkdir bin cd bin ln -s ../lush/bin/i686-pc-linux-gnu/lush .assuming that the bin directory in your homedir is in your search path. The alternative is to do cd cd lush sudo make installto perform a system-wide installation. The course will include a short tutorial on the Lush language, a simple interpreted language for numerical applications.
Implementing the Perceptron Algorithm, MSE Classifier (linear regression), Logistic Regression. Details and datasets below:
: Click on this links to get the homework: hw-backprop.tgz. Due Date is Tuesday October 26th.
Click on this links to get the homework: hw-unsup.tgz. Due Date is Tuesday November 9th.
click here for a list of final projects. Projects can be done individually, in groups of 2, or (with permission) in groups of 3. If you have an interest in a particular topic, you can propose your own project topic, subject to approval by the instructor. Send a description of your project proposal to the instructor and the TA before November 9th. Otherwise, a list of possible projects will be proposed during the course of the semester (early November). All projects are due December 20th. There will be a project showcase and demo show on Tuesday December 14th on the 13th floor of Warren Weaver Hall from 5:00 to 9:00 PM. Extra points will be given to those making a presentation (poster and/or demo) at the project showcase. You must send a .tar or .tgz file to the TA with your code and a PDF file describing your project and the results you obtained. It is expected that you will implement a learning algorithm yourself, or use an existing one in a new and interesting way. In other words, merely downloading an SVM package off the web and running it on a standard dataset won't get you a good grade. On a few occasions, some class projects have been known to turn into conference publications....
A more extensive list is coming soon. Among other topics, we are looking for volunteers to implement various standard learning algorithms and demos for the C++ library eblearn.
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