Probabilistic Machine Learning :
1. A Deep Learning Tutorial: From Perceptrons to Deep Networks by Ivan Vasilev.
2. Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa.
3. Basic introduction to machine learning. by Professor Andrew Ng.
4. Introduction to machine learning. by Nando de Freitas.
5. Introduction to machine learning. by Nando de Freitas.
Courses/Slides:
1. Probabilistic Machine Learning, CS772A/CS698X,Dr. Piyush Rai, Winter 2016, https://www.cse.iitk.ac.in/users/piyush/courses/pml_winter16/PML.html.
2. Probabilistic Machine Learning, CS772A/CS698X,Dr. Piyush Rai, Fall 2017, https://www.cse.iitk.ac.in/users/piyush/courses/pml_fall17/pml_fall17.html.
3. Bayesian Machine Learning, CS698S,Dr. Piyush Rai, Winter 2017, https://www.cse.iitk.ac.in/users/piyush/courses/bml_winter17/bayesian_ml.html.
4. Variational Autoencoders Explained, Kevin Frans, http://kvfrans.com/variational-autoencoders-explained/.
5. Tutorial on Multimodal Machine Learning, Louis-Philippe (LP) Morency, Tadas Baltrusaitis , https://www.cs.cmu.edu/~morency/MMML-Tutorial-ACL2017.pdf.
Videos:
1. Generative Models,CS231n, Stanford University School of Engineering, https://www.youtube.com/watch?v=5WoItGTWV54&t=0s&index=14&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk.
2. Tutorial on Generative adversarial networks, ICCV17 Tutorial on Generative adversarial networks, https://www.youtube.com/watch?v=sgHdUYHGvtA&list=PL_bDvITUYucDEzjMTgh1cgtTIODZe3prZ.
Papers:
1. Auto-Encoding Variational Bayes, Diederik P Kingma, Max Welling, https://arxiv.org/pdf/1312.6114.pdf.
2. Tutorial on Variational Autoencoders, Carl Doersch, https://arxiv.org/pdf/1606.05908.pdf.
3. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel, https://arxiv.org/pdf/1606.03657.pdf.
4. Joint Multimodal Learning with Deep Generative Models, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo, https://arxiv.org/pdf/1611.01891.pdf
5. Objects that Sound, Relja Arandjelović, Andrew Zisserman, https://arxiv.org/pdf/1712.06651.pdf
6. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, Yarin Gal, Zoubin Ghahramani, https://arxiv.org/pdf/1506.02158.pdf.
7. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner, https://openreview.net/pdf?id=Sy2fzU9gl.
8. Few-shot Classification by Learning Disentangled Representations, Emiel Hoogeboom, Master Thesis, University of Amsterdam (2017), https://esc.fnwi.uva.nl/thesis/centraal/files/f1209468632.pdf
9. Learning Hierarchical Features from Generative Models, Shengjia Zhao, Jiaming Song,Stefano Ermon, https://arxiv.org/pdf/1702.08396.pdf
Books:
1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. http://www.deeplearningbook.org/.
2. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy https://mitpress.mit.edu/books/machine-learning-1
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