Awesome-Mirrors/awesome-courses
1
0
Fork 0
mirror of https://github.com/prakhar1989/awesome-courses.git synced 2024-06-29 04:52:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

add Introduction to Matrix Methods

Browse Source
This commit is contained in:
Arne Neumann 2015-02-07 17:23:07 +01:00
parent c3c97ac37b
commit e072d418d3
1 changed files with 6 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@ -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* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="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* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="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)