Machine Learning - Georgia Tech

Approx. 4 months

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff.

In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!

Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!

You will learn about and practice a variety of Supervised, Unsupervised and Reinforcement Learning approaches.

Supervised Learning is an important component of all kinds of technologies, from stopping credit card fraud, to finding faces in camera images, to recognizing spoken language. Our goal is to give you the skills that you need to understand these technologies and interpret their output, which is important for solving a range of data science problems. And for surviving a robot uprising.

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 section focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data.

Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers, and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization. This section also includes important Reinforcement Learning approaches like Markov Decision Processes and Game Theory.

A strong familiarity with Probability Theory, Linear Algebra and Statistics is required. An understanding of Intro to Statistics, especially Lessons 8, 9 and 10, would be helpful.

Students should also have some experience in programming (perhaps through Introduction to CS) and a familiarity with Neural Networks (as covered in Introduction to Artificial Intelligence).

See the Technology Requirements for using Udacity.

• Lesson 0: Machine Learning is the ROX
• Lesson 1: Decision Trees
• Lesson 2: Regression and Classification
• Lesson 3: Neural Networks
• Lesson 4: Instance-Based Learning
• Lesson 5: Ensemble B&B
• Lesson 6: Kernel Methods and Support Vector Machines (SVM)s
• Lesson 7: Computational Learning Theory
• Lesson 8: VC Dimensions
• Lesson 9: Bayesian Learning
• Lesson 10: Bayesian Inference

• Lesson 1: Randomized optimization
• Lesson 2: Clustering
• Lesson 3: Feature Selection
• Lesson 4: Feature Transformation
• Lesson 5: Information Theory

• Lesson 1: Markov Decision Processes
• Lesson 2: Reinforcement Learning
• Lesson 3: Game Theory
• Lesson 4: Game Theory, Continued