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Lecture 10 Decision Trees and Ensemble Methods | Stanford CS229 Machine Learning Autumn 2018

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Lessons List | 20 Lesson
Lecture 1 Welcome | Stanford CS229 Machine Learning Autumn 2018
01:15:20

Lecture 1 Welcome | Stanford CS229 Machine Learning Autumn 2018
Lecture 2 Linear Regression and Gradient Descent | Stanford CS229 Machine Learning Autumn 2018
01:18:17

Lecture 2 Linear Regression and Gradient Descent | Stanford CS229 Machine Learning Autumn 2018
Lecture 3 Locally Weighted Logistic Regression | Stanford CS229 Machine Learning Autumn 2018
01:19:34

Lecture 3 Locally Weighted Logistic Regression | Stanford CS229 Machine Learning Autumn 2018
Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018
01:22:02

Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018
Lecture 5 GDA Naive Bayes | Stanford CS229 Machine Learning Autumn 2018
01:18:52

Lecture 5 GDA Naive Bayes | Stanford CS229 Machine Learning Autumn 2018
Lecture 6 Support Vector Machines | Stanford CS229 Machine Learning Autumn 2018
01:20:57

Lecture 6 Support Vector Machines | Stanford CS229 Machine Learning Autumn 2018
Lecture 7 Kernels | Stanford CS229 Machine Learning Autumn 2018
01:20:25

Lecture 7 Kernels | Stanford CS229 Machine Learning Autumn 2018
Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018
01:23:26

Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018
Lecture 9 Approx Estimation Error ERM | Stanford CS229 Machine Learning Autumn 2018
01:26:03

Lecture 9 Approx Estimation Error ERM | Stanford CS229 Machine Learning Autumn 2018
Lecture 10 Decision Trees and Ensemble Methods | Stanford CS229 Machine Learning Autumn 2018
01:20:41

Lecture 10 Decision Trees and Ensemble Methods | Stanford CS229 Machine Learning Autumn 2018
Lecture 11 Introduction to Neural Networks | Stanford CS229 Machine Learning Autumn 2018
01:20:14

Lecture 11 Introduction to Neural Networks | Stanford CS229 Machine Learning Autumn 2018
Lecture 12 Backprop Improving Neural Networks | Stanford CS229 Machine Learning Autumn 2018
01:16:38

Lecture 12 Backprop Improving Neural Networks | Stanford CS229 Machine Learning Autumn 2018
Lecture 13 Debugging ML Models and Error Analysis | Stanford CS229 Machine Learning Autumn 2018
01:18:55

Lecture 13 Debugging ML Models and Error Analysis | Stanford CS229 Machine Learning Autumn 2018
Lecture 14 Expectation Maximization Algorithms | Stanford CS229 Machine Learning Autumn 2018
01:20:31

Lecture 14 Expectation Maximization Algorithms | Stanford CS229 Machine Learning Autumn 2018
Lecture 15 EM Algorithm Factor Analysis | Stanford CS229 Machine Learning Autumn 2018
01:19:48

Lecture 15 EM Algorithm Factor Analysis | Stanford CS229 Machine Learning Autumn 2018
Lecture 16 Independent Component Analysis RL | Stanford CS229 Machine Learning Autumn 2018
01:18:10

Lecture 16 Independent Component Analysis RL | Stanford CS229 Machine Learning Autumn 2018
Lecture 17 MDPs Value Policy Iteration | Stanford CS229 Machine Learning Autumn 2018
01:19:14

Lecture 17 MDPs Value Policy Iteration | Stanford CS229 Machine Learning Autumn 2018
Lecture 18 Continous State MDP Model Simulation | Stanford CS229 Machine Learning Autumn 2018
01:20:15

Lecture 18 Continous State MDP Model Simulation | Stanford CS229 Machine Learning Autumn 2018
Lecture 19 Reward Model Linear Dynamical System | Stanford CS229 Machine Learning Autumn 2018
01:21:07

Lecture 19 Reward Model Linear Dynamical System | Stanford CS229 Machine Learning Autumn 2018
Lecture 20 RL Debugging and Diagnostics | Stanford CS229 Machine Learning Autumn 2018
01:12:43

Lecture 20 RL Debugging and Diagnostics | Stanford CS229 Machine Learning Autumn 2018

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Course Description

Machine learning Field of study Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Is machine learning hard? Why is machine learning 'hard'? ... There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application.What is the goal of machine learning? Machine Learning Defined Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data.What are the basics of machine learning? Key Elements of Machine Learning Every machine learning algorithm has three components: Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.
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