- University of Oxford Deep Learning Course by Nando de Freitas, 2015:

- Lecture 1: Introduction
- Lecture 2: Linear models
- Lecture 3: Maximum likelihood and information
- Lecture 4: Regularization, model complexity and data complexity (part 1)
- Lecture 5: Regularization, model complexity and data complexity (part 2)
- Lecture 6: Optimization
- Lecture 7: Logistic Regression, A Torch Approach
- Lecture 8: Modular back-propagation, logistic regression and Torch
- Lecture 9: Neural networks and modular design in Torch
- Lecture 10: Convolutional Neural Networks
- Lecture 11: Max-margin learning, transfer and memory networks
- Lecture 12: Recurrent Neural Nets and LSTMs
- Lecture 13: Alex Graves on Hallucination with RNNs
- Lecture 14: Karol Gregor on Variational Autoencoders and Image Generation
- Lecture 15: Deep Reinforcement Learning – Policy search
- Lecture 16: Reinforcement learning and neuro-dynamic programming