Machine Learning: Unsupervised Learning
About this Course
This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.
Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!
Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for …
Machine Learning: Unsupervised Learning
About this Course
This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.
Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!
Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data.
Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.
The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).
Ever wonder how Netflix can predict what movies you’ll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!
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You will learn about and practice a variety of Unsupervised Learning approaches, including: randomized optimization, clustering, feature selection and transformation, and information theory.
,You will learn important Machine Learning methods, techniques and best practices, and will gain experience implementing them in this course through a hands-on final project in which you will be designing a movie recommendation system (just like Netflix!).
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lesson 1
Randomized optimization
Optimization, randomized
Hill climbing
Random restart hill climbing
Simulated annealing
Annealing algorithm
Properties of simulated annealing
Genetic algorithms
GA skeleton
Crossover example
What have we learned
MIMIC
MIMIC: A probability model
MIMIC: Pseudo code
MIMIC: Estimating distributions
Finding dependency trees
Probability distribution
lesson 2
Clustering
Clustering and expectation maximization
Basic clustering problem
Single linkage clustering (SLC)
Running time of SLC
Issues with SLC
K-means clustering
K-means in Euclidean space
K-means as optimization
Soft clustering
Maximum likelihood Gaussian
Expectation Maximization (EM)
Impossibility theorem
lesson 3
Feature Selection
Algorithms
Filtering and Wrapping
Speed
Searching
Relevance
Relevance vs. Usefulness
lesson 4
Feature Transformation
Feature Transformation
Words like Tesla
Principal Components Analysis
Independent Components Analysis
Cocktail Party Problem
Matrix
Alternatives
lesson 5
Information Theory
History -Sending a Message
Expected size of the message
Information between two variables
Mutual information
Two Independent Coins
Two Dependent Coins
Kullback Leibler Divergence
lesson 6
Unsupervised Learning Project
