### Lecture 1

Introduction to Deep Learning and Neural Networks

### Lecture 2

Learning with Neural Networks

### Lecture 3

Deeper into Deep Learning and Optimizations

### Lecture 4

Convolutional Neural Networks for Computer Vision

### Lecture 5

Understanding Convnets Visually and Intuitively

### Lecture 6

Convnets for Object detection, Segmentation

### Lecture 7

Group Equivariant Convnets (invited talk by T. Cohen)

### Lecture 8

Language Representations (invited talk by C. Monz)

### Lecture 9

Recurrent Neural Networks

### Lecture 10

Memory Networks and Recursive Networks

### Lecture 11

Bleeding edge deep learning (student presentations #1)

### Lecture 12

Bleeding edge deep learning (student presentations #2)

### Lecture 13

Restricted Boltzmann Machines, Autoencoders

### Lecture 14

Bayesian Inference, Graphical Models and Neural Networks