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).

### Syllabus

**WEEK 1**

Introduction

Introduction to the course - machine learning and neural nets

Graded: Lecture 1 Quiz

**WEEK 2**

The Perceptron learning procedure

An overview of the main types of neural network architecture

Graded: Lecture 2 Quiz

**WEEK 3**

The backpropagation learning proccedure

Learning the weights of a linear neuron

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

Graded: Lecture 4 Quiz

**WEEK 5**

Object recognition with neural nets

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

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.

Graded: Lecture 6 Quiz

**WEEK 7**

Recurrent neural networks

This module explores training recurrent neural networks

Graded: Lecture 7 Quiz

**WEEK 8**

More recurrent neural networks

We continue our look at recurrent neural networks

Graded: Lecture 8 Quiz

**WEEK 9**

Ways to make neural networks generalize better

We discuss strategies to make neural networks generalize better

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

Graded: Lecture 10 Quiz

**WEEK 11**

Hopfield nets and Boltzmann machines

Graded: Lecture 11 Quiz

**WEEK 12**

Restricted Boltzmann machines (RBMs)

This module deals with Boltzmann machine learning

Graded: Lecture 12 Quiz

**WEEK 13**

Stacking RBMs to make Deep Belief Nets

Graded: Programming Assignment 4: Restricted Boltzmann Machines

Graded: Lecture 13 Quiz

**WEEK 14**

Deep neural nets with generative pre-training

Graded: Lecture 14 Quiz

**WEEK 15**

Modeling hierarchical structure with neural nets

Graded: Lecture 15 Quiz

Graded: Final Exam

**WEEK 16**

Recent applications of deep neural nets