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CPSC 340: Machine Learning and Data Mining

CPSC 340: Machine Learning and Data Mining (2012, University of British Columbia). Instructor: Professor Nando de Freitas. This course will provide an introduction to machine learning and data mining. It will teach the basic principles and skills required for analysing data in a principled way: finding statistical patterns, dimensionality reduction, clustering, classification and prediction. Students will also have the opportunity of learning Python, a widely used programming language.

Lecture 13 - Learning Bayesian Networks

Go to the Course Home or watch other lectures:

Lecture 01 - Introduction to Machine Learning

Lecture 02 - Introduction to Machine Learning 2

Lecture 03 - Basic Probability

Lecture 04 - Introduction to Probability, Linear Algebra and Pagerank

Lecture 05 - Introduction to Bayes Rule

Lecture 06 - Bayes Rule and Bayesian Networks

Lecture 07 - Bayesian Networks, Probabilistic Graphical Models

Lecture 08 - Inference in Bayesian Networks and Dynamic Programming

Lecture 09 - Hidden Markov Models (HMMs)

Lecture 10 - Expectation, Probability and Bernoulli Models

Lecture 11 - Maximum Likelihood

Lecture 12 - Bayesian Learning

Lecture 13 - Learning Bayesian Networks

Lecture 14 - Linear Algebra Revision for Machine Learning and Web Search

Lecture 15 - Singular Value Decomposition (SVD)

Lecture 16 - Principal Component Analysis (PCA)

Lecture 17 - Linear Prediction

Lecture 18 - Least Squares and the Multivariate Gaussian

Lecture 19

Lecture 20 - Cross-validation, Big Data and Regularization

Lecture 21 - L1 Regularization and the Lasso

Lecture 22 - Sparse Models and Variable Selection

Lecture 23 - Dirichlet and Categorical Distributions

Lecture 24 - Text Classification with Naive Bayes

Lecture 25 - Twitter Sentiment Prediction with Naive Bayes

Lecture 26 - Optimization

Lecture 27 - Logistic Regression

Lecture 28 - Neural Networks

Lecture 29 - Neural Nets and Backpropagation

Lecture 30 - Deep Learning

Lecture 31 - Decision Trees

Lecture 32 - Random Forests

Lecture 33 - Random Forests, Face Detection and Kinect