Neural Networks for Machine Learning with Geoffrey Hinton


The course “Neural Networks for Machine Learning ” by Geoffrey Hinton of Toronto University, will be offered free of charge to everyone on the Coursera platform. Sign up at….

Neural Networks for Machine Learning

Geoffrey Hinton

Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

Workload: 7-9 hours/week

Previous Session:

Oct 1st 2012 (8 weeks long) Sign Up

About the Course

Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples.

Recommended Background

Programming proficiency in Matlab, Octave or Python. Enough knowledge of calculus to be able to differentiate simple functions. Enough knowledge of linear algebra to understand simple equations involving vectors and matrices. Enough knowledge of probability theory to understand what a probability density is.

Course Format

The class will consist of lecture videos, which are between 5 and 15 minutes in length. These contain 1-3 integrated quiz questions per video. There will also be standalone homework that is not part of video lectures, optional programming assignments, and a (not optional) final test.


  • Will I get a certificate after completing this class?Yes. Students who successfully complete the class will receive a certificate signed by the instructor.
  • What resources will I need for this class?You will need access to a computer that you can use to experiment with learning algorithms written in Matlab, Octave or Python. If you use Matlab you will need your own licence.
  • What is the coolest thing I’ll learn if I take this class?You will learn how a neural network can generate a plausible completion of almost any sentence.


About Geoffrey Hinton

Department of Computer Science
University of Toronto

Geoffrey Hinton designs machine learning algorithms. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. He received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member in Computer Science at Carnegie-Mellon. He then moved to the Department of Computer Science at the University of Toronto where he directs the program on “Neural Computation and Adaptive Perception” for the Canadian Institute for Advanced Research.Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. He is an honorary foreign member of the American Academy of Arts and Sciences, and a former president of the Cognitive Science Society. He has received honorary doctorates from the University of Edinburgh and the University of Sussex. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the IEEE Neural Network Pioneer award (1998), the Killam prize for Engineering (2012) and the NSERC Herzberg Gold Medal (2010) which is Canada’s top award in Science and Engineering.

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