Introduction to Machine Learning - thegreatcourses

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Introduction to Machine Learning – thegreatcourses Description

We live on a planet with billions of people—but also billions of computers, many of them programmed to evaluate and make decisions much as humans do. We don’t yet reside among truly intelligent machines, but they are getting there, and knowing how machines learn is crucial for everyone from professionals to students to ordinary citizens. Machine learning pervades our culture in a multitude of ways, through tools and practices from medical diagnosis and data management to speech synthesis and search engines.

An offshoot of artificial intelligence, machine learning takes programming a giant step beyond the traditional role of computers in routine data processing, such as scheduling, keeping accounts, and making calculations. Now computers are being programmed to figure out how to solve problems by themselves—problems that are so complex that humans often don’t know where to begin. Indeed, machine learning has become so advanced that, often, even the experts don’t know how a computer arrives at the solution it does.

Introduction to Machine Learning demystifies this revolutionary discipline in 25 try-it-yourself lessons taught by award-winning educator and researcher Michael L. Littman, the Royce Family Professor of Teaching Excellence in Computer Science at Brown University. Dr. Littman guides you through the history, concepts, and techniques of machine learning, using the popular computer language Python to give you hands-on experience with the most widely used programs and specialized libraries .

For those new to Python, this course includes a lecture that is a dedicated tutorial on how to get started with this versatile, easy-to-use language. Professor Littman includes approximately one Python demonstration in each lesson. Even if you have never written code in Python, or any language, you can still run these programs for yourself to get a feeling for the amazing power of machine learning.

Dig into the Details

In Introduction to Machine Learning, you investigate three major types of representational spaces, focusing on the types of problems they excel at solving.

Decision Trees: Anyone who has dealt with a phone menu has faced a decision tree. “For sales, press 1. For accounts, press 2.” Each choice is followed by additional choices, until you get the person or department you want. Decision trees are a natural fit for machine-learning problems that require “if-then” reasoning, such as many medical diagnoses.
Bayesian Networks: In contrast to decision trees, which rely on a sequence of deductions, Bayesian networks involve inferences from probability. They are well-suited to cases where you need to work backwards from the data to their likely causes. A prominent example is software that identifies probable spam messages.
Neural Networks: Designed to work like neurons in the brain, neural networks excel at perceptual tasks, such as image recognition, language processing, and data classification. Deep neural networks are composed of networks of networks and are the heart of the “deep learning” revolution that Professor Littman covers in detail.