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Open Courseware
Machine Learning
This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML techniques are widely applied in engineering, science, finance, and commerce to build systems for which […]
https://academicearth.org/courses/machine-learning-1/ -
Open Courseware
Advice for Applying Machine Learning
Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)
http://www.youtube.com/watch?v=UFH5ibWnA7g -
Open Courseware
The Motivation & Applications of Machine Learning
The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning
http://www.youtube.com/watch?v=UzxYlbK2c7E -
Open Courseware
Machine Learning
Note: This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative […]
https://academicearth.org/courses/machine-learning/ -
Open Courseware
Three Learning Principles
Three Learning Principles – Major pitfalls for machine learning practitioners; Occam’s razor, sampling bias, and data snooping.
http://www.youtube.com/watch?v=EZBUDG12Nr0 -
Open Courseware
Support Vector Machines
Support Vector Machines – One of the most successful learning algorithms; getting a complex model at the price of a simple one.
http://www.youtube.com/watch?v=eHsErlPJWUU -
Open Courseware
Epilogue
Epilogue – The map of machine learning. Brief views of Bayesian learning and aggregation methods.
http://www.youtube.com/watch?v=ihLwJPHkMRY -
Open Courseware
Radial Basis Functions
Radial Basis Functions – An important learning model that connects several machine learning models and techniques.
http://www.youtube.com/watch?v=O8CfrnOPtLc -
Open Courseware
Theory of Generalization
Theory of Generalization – How an infinite model can learn from a finite sample. The most important theoretical result in machine learning.
http://www.youtube.com/watch?v=6FWRijsmLtE -
Open Courseware
Bayesian Statistics and Regularization
Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example – Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem
http://www.youtube.com/watch?v=sQ8T9b-uGVE