CS 229: Machine learning- Notes on Lecture #1
With this blog post, I am making a bold attempt to follow an online course to a decent degree of successful completion. I shall post some notes, links to relevant lecture notes and other misc. stuff as I start on my quest to follow the CS 229: Machine Learning course from Stanford University.
This is the video lecture:
My summarized notes:
Machine learning definition: Arthur Samuel(1959), Tom Mitchel(1998)
In the rest of the lecture, the four main parts of the course is described in some detail along with illustrative examples.
Supervised learning: providing the algorithm a dataset, supervising- learn the association between input & output, regression problems, classification problems, support vector machines- infinite number of features
Unsupervised learning: clustering, Cocktail party problem, indepedent component analysis
Reinforcement learning: reward function (good dog, bad dog), feedback function
Before proceeding further, you may also want to take a look at the course materials. The Lecture notes and the video lectures are not in a 1-1 mapping, its a many-one mapping. So you have 20 video lectures and 12 lecture notes. Corresponding to the first video lecture, there is no corresponding lecture note.
The first of the Section Notes on Linear Algebra carries a nice review of the concepts that will be useful for a quick review. From what I understand and know, Linear Algebra seems to very important to the study of Machine Learning. So, its a good idea to just skim over the notes given on the website.
Hope you enjoy following the first video lecture! See you.