Mathematics and Applications of Machine Learning
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Version: 2018-09-05
SS18 Exam Results
Contents:
Mathematics and Applications of Machine Learning
Description
About this course material
License
Introduction
References to start with
Vocabulary and Overview
First steps: Linear Classification
Binary Classification Problems
Perceptron
Adaline
Where to go from here?
Sigmoid activation function
Support Vector Machine
First Non-Linear Classification
Multi-Class Classification
Optimization Theory
Existence of optimal solutions
Convex optimization
Lagrange function and Duality
Nonlinear Classification Problems
Kernel Trick for Support Vector Machines
Multi-Layer Networks and Backpropagation
References
Source Code
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Repository:
GitHub
Student Projects:
GitHub
Indices and tables
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Index
Search Page
Mathematics and Applications of Machine Learning
Navigation
Mathematics and Applications of Machine Learning
Introduction
First steps: Linear Classification
Where to go from here?
Optimization Theory
Nonlinear Classification Problems
References
Related Topics
Documentation overview
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