lectures

This page will have links to lecture notes from the class
  • lecture 01  Intro/Course overview
  • lecture 02  Vectors and Matrices in Matlab/Octave
  • lecture 03  Descriptive Stats, Reading Files, Array operations
  • lecture 04  Matrices, Linear Algebra, and Eqn solving
  • lecture 04.5  Image Processing (guest lecture by Prof. Hong)
  • lecture 05  File I/O, For loops, Function Definitions
  • lecture 06  Function Defs, If/While, arrayfun
  • lecture 07  Taylor series, Error in Numerical Computation
  • lecture 08  Plotting log relative error of Taylor Approximations
  • lecture 09 Test-driven development, switch, try/catch
  • lecture 10  Parameter passing, scope, local/global variables
  • lecture 11  Function handles, anonymous functions, nested fns
  • lecture 11.5  Edge Detection (guest lecture by Prof. Hong)
  • lecture 12  Global Vars, Persistent Vars, Scope, Load Path
  • lecture 13  Persistent/static variables, zen.do demo
  • lecture 14  nested fns vs sub functions, error handling
  • lecture 15  polynomials, roots/poly, conv/deconv, polyder
  • lecture 16  Curve-fitting by polynomials, probability distributions
  • lecture 17  Vandermonde matrix and use in polyfit
  • lecture 18  left division in matlab and curve fitting
  • lecture 19  curve fitting by reduction to polys, interpolation
  • lecture 20  3d plotting 
  • lecture 21   ODEs and their use in Modeling Physical Systems
  • lecture 22  Simulating ODEs using Matlab/Octave
  • lecture 23  Using LSODE to Solve the Predator/Prey model
  • lecture 24  The Jacobian and Solution methods for Stiff ODEs
  • lecture 25   Non-linear equation solving using fsolve and sqp
  • lecture 26  Latex and Mathematical Typesetting
  • lecture 27  Latex-  figures, references, code, images, ...
  • lecture 28  Symbolic Mathematical Computing using SAGE
  • lecture 29  Collaborative Source Control using GIT
  • lecture 30  Public/Private Key Cryptosystems and Computer Security
  • lecture 31  Branching and Merging in Local and Remote respositories with GIT
  • lecture 32  Introduction to R - basic statistical analysis
  • lecture 33  Exploratory Data Analysis in R: tree classifiers, k-means clustering, and PCA
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