Machine Learning (Udacity)

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.

What You Will Learn

LESSON 1
Supervised Learning
Machine Learning is the ROX
Decision Trees
Regression and Classification
Neural Networks
Instance-Based Learning
Ensemble B&B
Kernel Methods and Support Vector Machines (SVM)s
Computational Learning Theory
VC Dimensions
Bayesian Learning
Bayesian Inference

LESSON 2
Unsupervised Learning
Randomized optimization
Clustering
Feature Selection
Feature Transformation
Information Theory

LESSON 3
Reinforcement Learning
Markov Decision Processes
Reinforcement Learning
Game Theory

Prerequisites and Requirements
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).