*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.
If you are new to Machine Learning, we suggest you take these 3 courses in order.
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).
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!).
### 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: 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 4: Feature Transformation
- Feature Transformation
- Words like Tesla
- Principal Components Analysis
- Independent Components Analysis
- Cocktail Party Problem
### Lesson 5: Information Theory
-Sending a Message
- Expected size of the message
- Information between two variables
- Mutual information
- Two Independent Coins
- Two Dependent Coins
- Kullback Leibler Divergence
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Vinayak Mehtaaudited this course, spending 10 hours a week on it and found the course difficulty to be hard.
The way in which the instructors teach is awesome.
This is a masters level machine learning course. I would recommend taking this course at a slow pace if you're a beginner in the machine learning domain, making sure that you get a thorough understanding of the material.