Course Description
In the era of data deluge its important to separate relevant from irrelevant, i.e. like segregating chaff from the grains. Dimension Reduction techniques are widely used to identify relevant features (or combinations) representing the underlying structure of the data.
What am I going to get from this course?
• Understand dimension reduction techniques, problems associated with it, and its practical applications.
• Understand techniques to reduce dimensions without harming important variables
• Learn application of Dimension Reduction techniques for practical problems
Prerequisites and Target Audience
What will students need to know or do before starting this course?
• Familiarity with Linear Algebra, Probability, Statistics
• Familiarity with Computer Programming, preferably Python.
Who should take this course? Who should not?
Students or professionals with formal college education in Science and Mathematics
Curriculum
Module 1: Background
53:03
Lecture 1
Introduction
46:46
Lecture 2
Data Pre-Processing
06:17
Module 2: Dimensionality Reduction and Representation
01:03:58
Lecture 3
Principal Component Analysis I
14:56
Lecture 4
Principal Component Analysis II
12:00
Lecture 5
Principal Component Analysis III
04:22
Lecture 6
Sparse Coding
09:18
Lecture 7
Independent Component Analysis
14:50
Lecture 8
Self Organizing Maps
08:32
Lecture 9
Mini Project I
04:44
Lecture 10
Mini-Project II
05:51
Lecture 11
Assignment (Homework)
09:58
Lecture 12
References
00:24
