Summary
An introductory course using the mathematical programming language MATLAB to introduce first year and more advanced students to: (1) the use of MATLAB in scientific work, (2) basic methods of data analysis such as classification, dimension-reduction, (3) statistical inference (parametric and non-parametric), and (4) techniques for modeling data with an emphasis on Bayesian approaches.
- Terms: Term 3 in academic year 07-08 starting 02.15.08 and ending on 04.18.08 which is midpoint of Term 4 (taught totally in Term 4 in academic year 08-09)
- Day of the week: MWF
- Start time - Stop time: 2pm - 4pm
Announcements
No class on Wednesday April 16th or Friday April 18th. Please hand in all assignments by Tuesday April 22nd.
Description
The course provides a collection of basic and useful quantitative skills which nearly all graduate students need. Use of MATLAB, data reduction techniques (e.g. principal components analysis), factorization methods (e.g. singular value decomposition), spectral methods, classification techniques (cluster analysis, support vector machines, etc.), and an introduction to Bayesian inference and modeling. This course is 'hands-on' and will use two or three basic data sets derived from human imaging experiments. The same data sets will be used throughout the entire course to facilitate familiarity with the data and therefore focus students on the techniques being taught. Although neuroimaging data will be used, the techniques learned are applicable to a wide range of data types including biochemical networks, gene networks, and general signal processing. The course starts at 'ground zero' and introduces MATLAB from a naive user's perspective.
Grading
Grade (A-F) = 1/2 (projects) + 1/2 (quizzes). Students will work on two Projects - (1) Due March 17, 2008. (2) Due April 14, 2008. There will be 3 quizzes given throughout course. Final grade takes both project and quizzes into account.
Please hand in all assignments by Tuesday April 22nd.