Neural Networks for Machine Learning

Geoffrey Hinton, University of Toronto

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.

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.

  • 1 October 2012, 8 weeks
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
  • Audio:
  • Email-course:
  • Language: English Gb


No reviews yet. Want to be the first?

Register to leave a review

Included in selections:
Small-icon.hover Machine Learning
Machine learning: from the basics to advanced topics. Includes statistics...
Small-icon.hover Deep Learning
Good materials on deep learning.
More on this topic:
Mas-632f08 Conversational Computer Systems
This class explores interaction with mobile computing systems and telephones...
Uoft_logo Introduction to Machine Learning (CSC2515, Fall 2008)
Introductory course in machine learning by world leading expert Geoffrey Hinton...
Xylscejy8t3gmrnlodrerh8kpnqaxng8ofy9aj8hjhm44-hvyisis32yy2rqknta3syn4yxuegk7nnjtpg=s0#w=436&h=268 Intro to Algorithms. Social Network Analysis
This class will give you an introduction to the design and analysis of algorithms...
9-67s01 Object and Face Recognition
Provides a comprehensive introduction to key issues and findings in object recognition...
9-012s02 The Brain and Cognitive Sciences II (Spring 2002)
This course is the second half of the intensive survey of brain and behavioral...
More from 'Mathematics, Statistics and Data Analysis':
D6fd079b-e8c1-476c-97d9-d7e4bbb1ecc4-49754ce235f3.small Predictive Analytics Final Project
Apply your predictive modelling acumen in a business case setting. The final...
C5a80d47-db96-407b-8034-2df8629a5dc3-5fc4c90a5aad.small LAFF – On Programming for Correctness
Learn to apply formal methods to systematically develop correct, loop-based...
Cae5395c-5179-4e6c-9e6c-873eb8f77d21-307674076932.small Advanced Linear Algebra: Foundations to Frontiers
Learn advanced linear algebra for computing. Linear algebra is one of the fundamental...
48708e7e-0152-4a4b-ad4c-2dadce29c87f-1b12f52e380e.small Data Analysis in Social Science—Assessing Your Knowledge
Learn the methods for harnessing and analyzing data to answer questions of cultural...
1cac89b9-58b6-4f8b-8cee-0a2f7feded60-9ed0da58fa14.small FA20: Introduction to Analytics Modeling
Learn essential analytics models and methods and how to appropriately apply...
More from 'Coursera':
Success-from-the-start-2 First Year Teaching (Secondary Grades) - Success from the Start
Success with your students starts on Day 1. Learn from NTC's 25 years developing...
New-york-city-78181 Understanding 9/11: Why Did al Qai’da Attack America?
This course will explore the forces that led to the 9/11 attacks and the policies...
Small-icon.hover Aboriginal Worldviews and Education
This course will explore indigenous ways of knowing and how this knowledge can...
Ac-logo Analytic Combinatorics
Analytic Combinatorics teaches a calculus that enables precise quantitative...
Talk_bubble_fin2 Accountable Talk®: Conversation that Works
Designed for teachers and learners in every setting - in school and out, in...

© 2013-2019