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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 18 - Least Squares and the Multivariate Gaussian


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