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CPSC 540: Machine Learning (2013, University of British Columbia). Instructor: Professor Nando de Freitas.
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Description
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.
Course content
- Lecture 01 – Introduction to Machine Learning Unlimited
- Lecture 02 – Linear Prediction Unlimited
- Lecture 03 – Maximum Likelihood and Linear Regression Unlimited
- Lecture 04 – Regularization and Regression Unlimited
- Lecture 05 – Regularization, Cross-validation and Data Size Unlimited
- Lecture 06 – Bayesian Learning Unlimited
- Lecture 07 – Bayesian Learning (cont.) Unlimited
- Lecture 08 – Introduction to Gaussian Processes Unlimited
- Lecture 09 – Gaussian Processes Unlimited
- Lecture 10 – Bayesian Optimization and Multi-armed Bandits Unlimited
- Lecture 11 – Decision Trees Unlimited
- Lecture 12 – Random Forests Unlimited
- Lecture 13 – Random Forests Applications Unlimited
- Lecture 14 – Unconstrained Optimization Unlimited
- Lecture 15 – Logistic Regression and Neuron Models Unlimited
- Lecture 17 – Neural Networks Unlimited
- Lecture 18 – Deep Learning Unlimited
- Lecture 19 – Deep Learning (cont.), Google Autoencoders and Dropout Unlimited
- Lecture 20 – Importance Sampling and Markov Chain Monte Carlo (MCMC) Unlimited
- Lecture 21 – Markov Chain Monte Carlo (MCMC) II Unlimited
Instructor
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