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CPSC 540: Machine Learning

CPSC 540: Machine Learning (2013, University of British Columbia). Instructor: Professor Nando de Freitas. This is a graduate-level course on machine learning, a field that focuses on using automated data analysis for tasks like pattern recognition and prediction. Topics will (roughly) include linear models, density estimation, graphical models, Bayesian methods, deep learning, online/active/causal learning, reinforcement learning, and learning theory.

Lecture 05 - Regularization, Cross-validation and Data Size

Go to the Course Home or watch other lectures:

Lecture 01 - Introduction to Machine Learning

Lecture 02 - Linear Prediction

Lecture 03 - Maximum Likelihood and Linear Regression

Lecture 04 - Regularization and Regression

Lecture 05 - Regularization, Cross-validation and Data Size

Lecture 06 - Bayesian Learning

Lecture 07 - Bayesian Learning (cont.)

Lecture 08 - Introduction to Gaussian Processes

Lecture 09 - Gaussian Processes

Lecture 10 - Bayesian Optimization and Multi-armed Bandits

Lecture 11 - Decision Trees

Lecture 12 - Random Forests

Lecture 13 - Random Forests Applications

Lecture 14 - Unconstrained Optimization

Lecture 15 - Logistic Regression and Neuron Models

Lecture 16

Lecture 17 - Neural Networks

Lecture 18 - Deep Learning

Lecture 19 - Deep Learning (cont.), Google Autoencoders and Dropout

Lecture 20 - Importance Sampling and Markov Chain Monte Carlo (MCMC)

Lecture 21 - Markov Chain Monte Carlo (MCMC) II