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Last updated:

September 25, 2023

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

Badge on Completion

Certificate of completion

Unlimited Duration

Description

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.

Course Curriculum

  • 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

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