About this course: 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. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).

Created by: University of Toronto

Taught by: Geoffrey Hinton, Professor
Department of Computer Science

Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.5 stars Average User Rating 4.5See what learners said

Syllabus

WEEK 1

Introduction

Introduction to the course - machine learning and neural nets

5 videos, 8 readings

Graded: Lecture 1 Quiz

WEEK 2

The Perceptron learning procedure

An overview of the main types of neural network architecture

5 videos, 1 reading

Graded: Lecture 2 Quiz

WEEK 3

The backpropagation learning proccedure

Learning the weights of a linear neuron

5 videos, 1 reading

Graded: Lecture 3 Quiz

Graded: Programming Assignment 1: The perceptron learning algorithm.

WEEK 4

Learning feature vectors for words

Learning to predict the next word

5 videos, 1 reading

Graded: Lecture 4 Quiz

WEEK 5

Object recognition with neural nets

In this module we look at why object recognition is difficult.

4 videos, 1 reading

Graded: Lecture 5 Quiz

Graded: Programming Assignment 2: Learning Word Representations.

WEEK 6

Optimization: How to make the learning go faster

We delve into mini-batch gradient descent as well as discuss adaptive learning rates.

5 videos, 1 reading

Graded: Lecture 6 Quiz

WEEK 7

Recurrent neural networks

This module explores training recurrent neural networks

5 videos, 1 reading

Graded: Lecture 7 Quiz

WEEK 8

More recurrent neural networks

We continue our look at recurrent neural networks

3 videos, 1 reading

Graded: Lecture 8 Quiz

WEEK 9

Ways to make neural networks generalize better

We discuss strategies to make neural networks generalize better

6 videos, 1 reading

Graded: Lecture 9 Quiz

Graded: Programming assignment 3: Optimization and generalization

WEEK 10

Combining multiple neural networks to improve generalization

This module we look at why it helps to combine multiple neural networks to improve generalization

5 videos, 1 reading

Graded: Lecture 10 Quiz

WEEK 11

Hopfield nets and Boltzmann machines

5 videos, 1 reading

Graded: Lecture 11 Quiz

WEEK 12

Restricted Boltzmann machines (RBMs)

This module deals with Boltzmann machine learning

5 videos, 1 reading

Graded: Lecture 12 Quiz

WEEK 13

Stacking RBMs to make Deep Belief Nets

3 videos, 1 reading

Graded: Programming Assignment 4: Restricted Boltzmann Machines

Graded: Lecture 13 Quiz

WEEK 14

Deep neural nets with generative pre-training

5 videos, 1 reading

Graded: Lecture 14 Quiz

WEEK 15

Modeling hierarchical structure with neural nets

6 videos, 1 reading

Graded: Lecture 15 Quiz

Graded: Final Exam

WEEK 16

Recent applications of deep neural nets

3 videos

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How It Works

Coursework

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Creators

University of Toronto

Established in 1827, the University of Toronto has one of the strongest research and teaching faculties in North America, presenting top students at all levels with an intellectual environment unmatched in depth and breadth on any other Canadian campus.

Ratings and Reviews

Rated 4.5 out of 5 of 347 ratings

good , theoretical,but no new neural network technology . but ,if you understand the theoretical content , you will have one steady base to understand other new technology.

Programming exercises could be made more clear. Also, prior to each lecture, it would be useful to mention what specific papers or back ground material should be read to follow the lectures easily.

Definitely not an introductory course to machine learning, but even if you know nothing about the matter (like me) and have a fairly good understanding of mathematics, or are willing to use your spare time to catch up with linear algebra and calculus topics (again, like myself), you can still enjoy this course very much.

This was a superb course. I completely enjoyed it. There were a few technical issues with the assignments. But other than that, it was a top notch course.

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Neural Networks for Machine Learning