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 |
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, 2 readings
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
FAQs
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Coursework
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University of Toronto
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Ratings and Reviews
For a beginner in machine learning the explanations given in the videos might be difficult to understand. Particularly, for some quizzes complementary information from other sources has been necessary. The programming assignments helped a lot to understand the theory and to get a deeper insight. All in all a very good course on neural networks. I have learned a lot.
This course is a great introduction to Machine Learning.
I would give it 5 stars, except for a huge problem with one week being a brick wall.
Week 13 of this course has a long-standing series of errors. They've been pointed out 4 months ago, but no updates have been made. Much of the missing information is required for that week's test, also that week's lectures do not explain how to do the math required on the test section. Be prepared to read raw research papers to learn how to do the math involved, as it is not taught within the course.
Not the best one for beginners
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very great course. I have gained lots of insights from this.