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