From e072d418d3e178d849284b0640ac2a8ac7ef2d2d Mon Sep 17 00:00:00 2001 From: Arne Neumann Date: Sat, 7 Feb 2015 17:23:07 +0100 Subject: [PATCH] add Introduction to Matrix Methods --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 8ae5227..1bd232a 100644 --- a/README.md +++ b/README.md @@ -319,6 +319,12 @@ Courses - Taught by one of the leading experts on Machine Learning - **Tom Mitchell** - [Lectures](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) - [Project Ideas and Datasets](http://www.cs.cmu.edu/~tom/10701_sp11/proj.shtml) +- [EE103](http://stanford.edu/class/ee103/) **Introduction to Matrix Methods** *Stanford University* Assignments Lecture Notes Readings + - The course covers the basics of matrices and vectors, solving linear equations, least-squares methods, and many applications. It'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. EE103 is based on a book that [Stephen Boyd](http://stanford.edu/~boyd/) and [Lieven Vandenberghe](http://www.seas.ucla.edu/~vandenbe/) are currently writing. Students will use a new language called [Julia](http://julialang.org/) to do computations with matrices and vectors. + - [Lectures](http://stanford.edu/class/ee103/lectures.html) + - [Book](http://stanford.edu/class/ee103/mma.html) + - [Assignments](http://stanford.edu/class/ee103/homework.html) + - [Code](http://stanford.edu/class/ee103/julia_files) - [CS 109](http://cs109.github.io/2014/) **Data Science** *Harvard University* Assignments Lecture Notes Readings - Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. - [Lectures](http://cm.dce.harvard.edu/2015/01/14328/publicationListing.shtml)