Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade we have seen self-driving cars, practical speech recognition, effective web search and an improved understanding of the human genome. Machine learning is so prevalent today that you probably use without knowing it. Researchers think it is the best way to make progress towards cognitive AI.

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. You will learn about the theoretical learning and the practical knowledge needed to quickly and powerfully apply these techniques to problems.

This course provides a broad introduction to machine learning and statistical pattern recognition.

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms.

Topics include:

Supervised learning

• Parametric/ non-parametric algorithms
• Support Vector Machines
• Kernels
• Neural Networks

Unsupervised learning

• Clustering
• Dimensionality reduction
• Recommender systems
• Deep learning

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms.