Machine Learning: Unsupervised Learning (Udacity)

Machine Learning: Unsupervised Learning (Udacity)
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We recommend you take Machine Learning 1: Supervised Learning prior to taking this course.
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Machine Learning: Unsupervised Learning (Udacity)
Conversations on Analyzing Data. 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.

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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).




What You Will Learn


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


Prerequisites and Requirements

This class will assume that you have programming experience as you will be expected to work with python libraries such as numpy and scikit. A good grasp of probability and statistics is also required. Udacity's Intro to Statistics, especially Lessons 8, 9 and 10, may be a useful refresher. An introductory course like Udacity's Introduction to Artificial Intelligence also provides a helpful background for this course.


Why Take This Course

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!).



MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Free Course
We recommend you take Machine Learning 1: Supervised Learning prior to taking this course.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.