This course is taught by Nando de Freitas.
http://www.cs.ubc.ca/~nando/
Course Link
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
Lecture 1: Introduction
Lecture 2: Linear prediction
Lecture 3: Maximum likelihood
Lectures 4 & 5: Regularizers, basis functions and cross-validation
Lecture 6: Optimisation
Lecture 7: Logistic regression
Lecture 8: Back-propagation and layer-wise design of neural nets
Lecture 9: Neural networks and deep learning with Torch
Lecture 10: Convolutional neural networks
Lecture 11: Max-margin learning and siamese networks
Lecture 12: Recurrent neural networks and LSTMs
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
Lecture 16: Reinforcement learning with action-value functions
lec01
https://www.evernote.com/l/ATSR7mjhcmhDpoK_o1tFddTu-Ckb_SrGbiY
lec02
https://www.evernote.com/l/ATTYZbKNIBVBfo-QSupzC5ulgu4S3el6KaQ
lec03
https://www.evernote.com/l/ATTRpr029uJDopWLVw3afVdT7A7kPd8sY2c
lec04&05
https://www.evernote.com/l/ATSMSAL6jlpP55eU1zS93CdZsO6qUmw6N-o
lec06
https://www.evernote.com/l/ATT44El6KwdIb6FC1uuVkym560xd0YxK1Kc
lec07
https://www.evernote.com/l/ATSLJwHYOs9Lhp8G_mX-oK89zaz4WMndOjg
lec08
https://www.evernote.com/l/ATRUFd9wjsZHN7t4fRGn9uC_sz0UaL1PxH4
lec09
https://www.evernote.com/l/ATTxA_VmTgdCbKWRZh6Hk_m20vuCkT9jCUE
lec10
https://www.evernote.com/l/ATQDiFwLfL9G8ZZq5KGdT0pRxN1rptIPStc
lec11
https://www.evernote.com/l/ATR-Hke9gqxK8rdx4t9JY0UEwua-yl8H7WM
lec12
https://www.evernote.com/l/ATSMVJr2mlpLobpChAyGEea_cZsYZb1sr5I
lec13&14 is for guest speaking from Deepmind
lec15
https://www.evernote.com/l/ATSEZq5fLnNAmZwAbO6ZCnQPD9n1Bfyynos