My solutions for the CaltechX Learning From Data course on edX.
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Daniel Martins 1675e14c20
2017-12-21 21:56:26 -02:00
extra/neural-networks Simple (but hopefully clean) implementation of backpropagation. 2013-12-13 20:45:21 -02:00
material Added homework questions and solution keys. 2013-12-07 12:18:44 -02:00
week-01 Removed old file. 2013-12-07 15:30:11 -02:00
week-02 Updated README title since the solutions are composed by both code and math. 2013-10-18 12:21:20 -03:00
week-03 Fixed typo. 2015-01-02 18:04:35 -02:00
week-04 Updated math solutions. 2013-10-28 20:30:40 -02:00
week-05 Added code for week #7. 2013-11-18 23:22:37 -02:00
week-06 Added solutions to homework #6. 2013-11-11 21:16:31 -02:00
week-07 Added support for soft-margin SVM. 2013-11-20 12:55:13 -02:00
week-08 Improved precision of separating hyperplane. 2013-11-25 21:45:28 -02:00
week-09-10 Added some comments. 2013-12-07 12:35:28 -02:00
.gitignore Replaced markdown-based math solutions for TeX. 2013-12-07 15:29:10 -02:00
LICENSE Added code for the first week. 2013-10-15 11:41:50 -03:00 Update 2017-12-21 21:56:26 -02:00

Learning From Data - Solutions

This is the code/math I wrote in order to solve most of the assignments of Learning From Data, a machine learning course by Caltech.

Disclaimer: Don't follow this material blindly, it might be wrong. Use your judgement.

A Note About The Honor Code

The solutions were released incrementally, one assignment at a time, after each deadline. According to the course staff, this is ok; in fact, the professor himself encourages the students to show their solutions.

To fully enjoy the course, do not look at the solutions provided here until you submit your answer.


How The Assignments Are Organized

Solutions for each week's homework are kept in its own directory. Each directory contains a README file that explains the main purpose of the homework along with the following subdirectories:

Figures and plots generated during my thinking process
Explanations and solutions involving math
Octave code that answers the more practical questions


If this project is useful for you, buy me a beer!

Bitcoin: bc1qtwyfcj7pssk0krn5wyfaca47caar6nk9yyc4mu


Code And Plots

Copyright (C) Daniel Fernandes Martins. Distributed under the New BSD License.

Homework Assignments

Copyright (C) Yaser Abu-Mostafa. All rights reserved.