Handouts

Cogsci 109 SS1_23

Frequently Asked Questions

Handouts

Handout name/# Description   Handout name/# Description
 Introduction to Matlab. Covers the basics from running Matlab to matrix manipulation and graphing hints on limits and continuity!
 A review and reference of the greek alphabet, mathematical symbols and operators which you will likely need for this course or in the future! Matlab examples from lecture 4 : plotting examples The example scripts are here for plotting: example1.m, example2.m, example3.m
 Review of mathematics relevant to this course (will continue to be updated throughout the quarter)  

From lecture 4

 A brief animation depicting a matrix transpose operation
    From lecture 5&6 : filtering  Low_Pass.m - a simple moving average filter implementation in matlab as a function - also serves as an example of how matlab functions work.

RLow_PassF07.m - a simple first order recursive low pass filter implementation in matlab as a function.
 A handout that describes lagrange interpolation    for discussion section 3d plotting and visualization
 interpolation example scientific papers    data sets for homework 3
A text which was written at UCSD by a Professor of computational science and engineering, Thomas R. Bewley. Covers many areas of useful numerical methods. A great reference    An example of creating a simple function in matlab, and how to call that function. This is a zipped up folder, so unzip the folder, and you'll have the function, a file to run it, and some data to load. Be sure to run 'Run_Me.m,' not the function directly, or you will get an error.
  • False Color Representation
 Data must be 'viewed' appropriately to expose salient features. Here are several strategies    how to make a custom colormap in matlab and use the meshgrid command
 An exerpt from Dan Olphe's book 'Computer Graphics for Design: From Algorithms to AutoCAD.' This chapter gives an explanation of color theory.    Handout for linear and nonlinear least squares partial differential equation derivation and matlab implementation. Also a second file which demonstrates and explains specifics of linear least squares (extendable to nonlinear polynomial fits) is linked here.
 An exerpt from Dan Olphe's book 'Computer Graphics for Design: From Algorithms to AutoCAD.' This chapter gives an explanation of various data fitting methods. We'll only be using some of these, but you can read more.    The example we wrote in class for linear interpolation of 2D data. It is well commented now.
 Heuristics, A*, etc    It is highly recommended that you learn how to create Latex (pronounced 'lay-tech') documents. Here are a couple of tutorial introductions (pdf's)
 here's an example of using cell arrays to create labels for plots to use in a loop (useful for homework4)    Scientific visualization and communication are important to understand and incorporate into whatever career you take.
Midterm review topics list, and practice midterm and solutions You can download the data set here for homework 4 (or from the assignments section)
A fairly useful online html book to read about neural networks and applications to learning, automata, pattern recognition, etc. Specific readings are assigned from a few sections of this book Replacement histogram function for your homework 4. The lab computers have a problem with the hist function. To use, make sure this code is in your matlab path, or in the same directory as your homework code
A brief history from the roots of computational machines and automata to modern times, linking philosophy, mechanical engineering, mathematics, and cognitive science An example of using matlab's nonlinear function minimization algorithm to fit functions which may be nonlinear in the parameters (ie y=a*sin(b*x)+c*x+d)
A fairly useful online html book to read about neural networks and applications to learning, automata, pattern recognition, etc. Specific readings are assigned from a few sections of this book Code to generate plots of objective functions used to test various function minimization algorithms (Rosenbrock and Himmelblau functions)
A brief history from the roots of computational machines and automata to modern times, linking philosophy, mechanical engineering, mathematics, and cognitive science gradient descent example code
Introduces neural networks, and goes further with the topics presented here.
a demonstration of a single unit perceptron and gradient descent with weight decay training algorithm, demonstrated in class
  • gradient descent worksheet and code
Practice gradient descent by going through this code, the reading in ch5 of numerical methods, and practicing several problems - ie make up A and b, and find the coefficients x
The practice final and solutions. The actual final will be about double the length. Here is a list of topics to review for the final. It clearly states what was from pre-midterm although the final IS CUMULATIVE

References

Note: No textbooks are required at this time, however there will be weekly PDF handouts, lecture notes, online books and tutorials assigned as reading. There will also be a few recommended texts.

Other References

Many of these books may have newer editions. The most up to date is often useful.