With the speed at which cognitive computing and AI are now developing, it is important to keep up with evolving concepts and trends. Courses have been developed at major institutions providing in-depth training opportunities. Many of these courses are available at low cost, or can be audited for free with materials online. Following is a series of Introductory Lectures for courses on AI that are freely available. The description provided in each case is from the course material. These lectures offer an entry point to the wealth of training that is now becoming available in this sector. Course videos can be used as an introduction, to bolster training programs, or to preview instruction prior to enrollment.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
Instructor: Prof. Pieter Abbeel
Excerpts from Artificial Intelligence for Robotics Course (Georgia Tech College of Computing)
Course Creator and Instructor Sebastian Thrun gives an overview of his Artificial Intelligence for Robotics course, part of Georgia Tech’s Online Master of Science in Computer Science. Thrun is Co-Founder and CEO of Udacity and a Stanford research professor.
Course: Knowledge Based Artificial Intelligence (Udacity)
Georgia Tech, Udacity, and AT&T have teamed up to offer an online Master´s degree in Computer Science—the first of its kind delivered through a MOOC platform. This is a whole new way to advance your knowledge and skills with advanced computer science classes. And there are flexible options for learning with us: you can apply for the full degree through Georgia Tech or take individual classes with the Udacity full course experience. Read on to learn more about your options and choose the path that works best for you.