stanford_ml_ps1 ===============
Stanford Machine Learning, Problem Set 1
This is my write-up and code for my solutions to the first problem set from Stanford's Engineering Everywhere course entitled "Machine Learning". You can find this course and others at the SEE website.
The file homework1.pdf contains my detailed write-up of the problems. This includes extra explanations and heuristics which are not asked for, but which I have worked through for my own benefit. Perhaps it can help others too. If you find an error or a better explanation, please let me know.
The only problem in this set requiring code is problem 2. In the file lwlr.ipynb is an interactive Python notebook. You can download interactive Python for free at ipython.org. Interactive Python is an interface that works through your web browser. It is the Python language with libraries like numpy and matplotlib in a format similar to Mathematica and Maple. Note that the starter files which you find on the course website are intended to be used in MATLAB or Octave, so by switching to Python, those starter files become unusable except for inspirational purposes. I chose to use Python instead of Octave or MATLAB because I have the impression that Python is the leading language for machine learning. Marcel Oliver also has a convincing argument to use Python.