Nando de Freitas - Channel

Nando de Freitas

Nando de Freitas

I am a machine learning professor at UBC. I am making my lectures available to the world with the hope that this will give more folks out there the opportunity to learn some of the wonderful things I have been fortunate to learn myself. Enjoy.

69 videos

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54:40

Deep Learning Lecture 15: Deep Reinforcement Learning - Policy search

56:04

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

43:18

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

53:59

Deep Learning Lecture 13: Alex Graves on Hallucination with RNNs

51:10

Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs

58:50

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

50:30

Deep Learning Lecture 10: Convolutional Neural Networks

53:47

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

44:31

Deep learning Lecture 7: Logistic regression, a Torch approach

52:56

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

58:58

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

58:19

Deep Learning Lecture 6: Optimization

40:27

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

1:12:59

Deep Learning Lecture 3: Maximum likelihood and information

48:01

Deep Learning Lecture 2: linear models

52:16

Deep Learning Lecture 1: Introduction

39:17

Machine learning - Markov chain Monte Carlo (MCMC) II

1:16:18

Machine learning - Importance sampling and MCMC I

1:12:03

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

1:15:05

Machine learning - Deep learning I

1:04:24

Machine learning - Neural networks

1:13:47

Machine learning - Logistic regression

1:16:19

Machine learning - Unconstrained optimization

51:42

Machine learning - Random forests applications

1:16:55

Machine learning - Random forests

1:06:06

Machine learning - Decision trees

1:20:30

Machine learning - Bayesian optimization and multi-armed bandits

1:17:28

Machine learning - Gaussian processes

1:18:55

Machine learning - Introduction to Gaussian processes

21:10

Machine learning - Bayesian learning part 2

1:17:40

Machine learning - Bayesian learning

58:47

Machine learning - regularization, cross-validation and data size

1:01:15

Machine learning - Regularization and regression

1:14:01

Machine learning - Maximum likelihood and linear regression

1:04:20

Machine learning - linear prediction

59:58

Machine learning - introduction

38:29

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

33:11

undergraduate machine learning 32: Random forests

39:42

undergraduate machine learning 31: Decision trees

50:05

undergraduate machine learning 30: Deep learning

40:14

undergraduate machine learning 29: Neural nets and backpropagation

26:20

undergraduate machine learning 28: Neural networks

51:13

undergraduate machine learning 27: Logistic regression

49:02

undergraduate machine learning 26: Optimization

48:39

undergraduate machine learning 25: Twitter sentiment prediction with Naive Bayes

43:49

undergraduate machine learning 24: Text classification with Naive Bayes

46:40

undergraduate machine learning 23: Dirichlet and categorical distributions

1:11:40

undergraduate machine learning 22: Sparse models and variable selection

20:20

undergraduate machine learning 21: L1 regularization and the lasso

1:30:53

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

51:07

undergraduate machine learning 19: Maximum likelihood for linear prediction

42:59

undergraduate machine learning 18: Least squares and the multivariate Gaussian

51:16

undergraduate machine learning 17: Linear prediction

51:32

undergraduate machine learning 16: Principal Component Analysis - PCA

51:56

undergraduate machine learning 15: Singular Value Decomposition - SVD

52:38

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

53:28

undergraduate machine learning 13: Learning Bayesian networks

53:10

undergraduate machine learning 12: Bayesian learning

32:55

undergraduate machine learning 11: Maximum likelihood

42:21

undergraduate machine learning 10: Expectation, probability and Bernoulli models

52:23

undergraduate machine learning 9: Hidden Markov models - HMM

49:50

undergraduate machine learning 8: Inference in Bayesian networks and dynamic programming

45:00

undergraduate machine learning 7: Bayesian networks, aka probabilistic graphical models

51:44

undergraduate machine learning 6: Bayes rule and Bayesian networks

9:30

undergraduate machine learning 5: Introduction to Bayes

53:55

undergraduate machine learning 4: Introduction to probability, linear algebra and pagerank

53:54

undergraduate machine learning 3: Basic probability

53:54

undergraduate machine learning 2: Introduction to machine learning 2

46:17

undergraduate machine learning 1: Introduction to machine learning