Deep Learning Lecture 15: Deep Reinforcement Learning - Policy search

Deep Learning Lecture 16: Reinforcement learning and neuro-dynamic programming

Deep Learning Lecture 14: Karol Gregor on Variational Autoencoders and Image Generation

Deep Learning Lecture 13: Alex Graves on Hallucination with RNNs

Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs

Deep Learning Lecture 11: Max-margin learning, transfer and memory networks

Deep Learning Lecture 10: Convolutional Neural Networks

Deep Learning Lecture 9: Neural networks and modular design in Torch

Deep learning Lecture 7: Logistic regression, a Torch approach

Deep Learning Lecture 8: Modular back-propagation, logistic regression and Torch

Deep Learning Lecture 5: Regularization, model complexity and data complexity (part 2)

Deep Learning Lecture 6: Optimization

Deep Learning Lecture 4: Regularization, model complexity and data complexity (part 1)

Deep Learning Lecture 3: Maximum likelihood and information

Deep Learning Lecture 2: linear models

Deep Learning Lecture 1: Introduction

Machine learning - Markov chain Monte Carlo (MCMC) II

Machine learning - Importance sampling and MCMC I

Machine learning - Deep learning II, the Google autoencoders and dropout

Machine learning - Deep learning I

Machine learning - Neural networks

Machine learning - Logistic regression

Machine learning - Unconstrained optimization

Machine learning - Random forests applications

Machine learning - Random forests

Machine learning - Decision trees

Machine learning - Bayesian optimization and multi-armed bandits

Machine learning - Gaussian processes

Machine learning - Introduction to Gaussian processes

Machine learning - Bayesian learning part 2

Machine learning - Bayesian learning

Machine learning - regularization, cross-validation and data size

Machine learning - Regularization and regression

Machine learning - Maximum likelihood and linear regression

Machine learning - linear prediction

Machine learning - introduction

undergraduate machine learning 33: Random forests, face detection and Kinect

undergraduate machine learning 32: Random forests

undergraduate machine learning 31: Decision trees

undergraduate machine learning 30: Deep learning

undergraduate machine learning 29: Neural nets and backpropagation

undergraduate machine learning 28: Neural networks

undergraduate machine learning 27: Logistic regression

undergraduate machine learning 26: Optimization

undergraduate machine learning 25: Twitter sentiment prediction with Naive Bayes

undergraduate machine learning 24: Text classification with Naive Bayes

undergraduate machine learning 23: Dirichlet and categorical distributions

undergraduate machine learning 22: Sparse models and variable selection

undergraduate machine learning 21: L1 regularization and the lasso

undergraduate machine learning 20: Cross-validation, big data and regularization

undergraduate machine learning 19: Maximum likelihood for linear prediction

undergraduate machine learning 18: Least squares and the multivariate Gaussian

undergraduate machine learning 17: Linear prediction

undergraduate machine learning 16: Principal Component Analysis - PCA

undergraduate machine learning 15: Singular Value Decomposition - SVD

undergraduate machine learning 14: Linear algebra revision for machine learning and web search

undergraduate machine learning 13: Learning Bayesian networks

undergraduate machine learning 12: Bayesian learning

undergraduate machine learning 11: Maximum likelihood

undergraduate machine learning 10: Expectation, probability and Bernoulli models