Unsupervised Learning Courses and Topics

Machine Learning: Unsupervised Learning

https://www.udacity.com/course/ud741

Syllabus

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

Unsupervised Learning Project