Oxford:

Lectures
 Lecture 1: Introduction slides Video
 Lecture 2: Linear prediction slides Video
 Lecture 3: Maximum likelihood slides.pdf Video
 Lectures 4 & 5: Regularizers, basis functions and cross-validation slides.pdf Video 1 Video 2
 Lecture 6: Optimisation slides.pdf Video
 Lecture 7: Logistic regression slides.pdf Video
 Lecture 8: Back-propagation and layer-wise design of neural nets slides.pdf Video
 Lecture 9: Neural networks and deep learning with Torch slides.pdf Video
 Lecture 10: Convolutional neural networks slides.pdf Video
 Lecture 11: Max-margin learning and siamese networks slides.pdf Video
 Lecture 12: Recurrent neural networks and LSTMs slides.pdf Video
 Lecture 13: Hand-writing with recurrent neural networks (Guest speaker: Alex Graves from Google Deepmind)
 Lecture 14: Variational autoencoders and image generation (Guest speaker: Karol Gregor from Google Deepmind)
 Lecture 15: Reinforcement learning with direct policy search slides.pdf
 Lecture 16: Reinforcement learning with action-value functions slides.pdf