Basic Matrix methods for Data Sciences

This is the plan for a small course I am taking, Basic Linear Algebra and Matrix techniques for data science. 
  
The following techniques will give you an ability to understand complex machine learning techniques better, it is the first step towards becoming a complete ML expert.We will be working hands on the below mentioned applications and many more while learning the math. You will be playing with the data, testing your creative imagination towards finding new dimensions and patterns within it.

1. Vectors and matrices in Data Mining and pattern recognition.

2. Linear regression techniques. Spaces, span, basis, dimensionality.

3. Orthogonality, Least Squares, QR decompostion, SVD

4. Data mining applications:

 4.1 Classification of Handwritten digits.
    4.1.1 How to classify Handwritten Digits
    4.1.2 Classification Using SVD Bases
    4.1.3 Tangent Distance

 4.2 Text Mining
    4.2.2 Preprocessing
    4.2.3 Vector Space Model
    4.2.4 Latent Semantic Indexing
    4.2.5 Clustering

 4.3 Building a recommender system for NETFLIX data. (from the scratch)

Here we have an opportunity to work with a great new open source language called JULIA.

   
 Will be adding few more applications based on machine learning techniques like
 PCA, CCA, Boosting techniques, Reinforcement learning, SVM etc
 depending on the groups interests.  Many more goodies on the cards.

We are supported and guided by experts, researchers and JULIA open source community.

Interested people kindly add your name and mailID and contact number in the below doc:

https://docs.google.com/spreadsheet/ccc?key=0AmTPAEM_gTnadEl1SFFDdlcwQnNvOTVnWG52NDR4T1E&usp=sharing