CS 36/136 | Numerical and Computational Tools for Applied Science |
Summer 2008
Prof. Hany Farid | farid@cs.dartmouth.edu | Sudikoff 159 | 646.2761
office hours are by appointment, or just stop by my office
This course provides a practical and principled coverage of
useful numerical and computational tools of use in many
disciplines. The first half of this course provides the
mathematical (linear algebra) and computing (Matlab) framework
upon which data analysis tools are presented. These tools
include data fitting, Fourier analysis, dimensionality
reduction, estimation, clustering, and pattern recognition.
This course is designed for undergraduate and graduate students
across the sciences and social sciences.
Linear Algebra and Its Applications (4th Edition), G. Strang
Pattern Classification (2nd Edition), R. Duda, P. Hart, D. Stork
Lecture Notes
Gilbert Strang's on-line Linear Algebra Course (18.06)
Matlab Tutorial
Statistical Pattern Recognition
- Matlab, part I
basic arithmetic; vectors and matrices; matrix arithmetic; control structures; input/output; scripts and functions - Linear Algebra, part I
vectors and matrices; solving Ax=b - Matlab, part II
data structures; 1-D plotting; 2-D images; 3-D surfaces; GUI programming - Linear Algebra, part II
vector spaces; linear independence; linear basis; singular value decomposition (SVD); eigenvalues and eigenvectors - Data Fitting
linear regression (least-squares); polynomial fitting; Bezier curves - Fourier Analysis
Fourier series; Fourier transform - Dimensionality Reduction
principal components analysis (PCA); multi-dimensional scaling (MDS); independent components analysis (ICA) - Estimation
least-squares estimation (LSE); total least-squares estimation (TLSE); maximum likelihood estimation (MLE); Bayesian estimation - Cluster Analysis
K-means; expectation/maximization (EM) - Pattern Recognition
linear discriminant analysis (LDA); support vector machines (SVM)
Homework (30%) | Midterm (35%) | Final (35%)
There will be weekly written and programming homeworks.
You cannot collaborate or copy in any way on exams.
On the computer programming portion of homeworks, you may
discuss general approaches with other students before you sit
down at the computer to write code. Once you are writing code,
however, your code must be written by you: any copying
(electronic or otherwise) of another person's code or code
fragments is a violation of the honor code - this includes code
from any web page (other than our class web page) that you find
on the web.
On the written portion of homeworks, you may discuss general
approaches with other students before you sit down to write out
your solutions. Once you are writing your solutions, however,
your work must be your own: any copying of another person's
solution, or portions of their solution, is a violation of the
honor code - this includes solutions from any web page (other
than our class web page) that you find on the web.
You must reference all sources of help and collaboration on each
homework. For example, if you talked with Jane Smith about your
homework (in any way), you must note this on your homework. If
you used a source outside of one provided by me or the class web
page, you must cite it on your homework. Not doing so is a
violation of the honor code.