Lectures Feb 2016

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