MLG 013 Shallow Algos 2

Machine Learning Guide

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8+ y ago 55:36

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Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/13

Support Vector Machines (SVM)

Purpose: Classification and regression.
Mechanism: Establishes decision boundaries with maximum margin.
Margin: The thickness of the decision boundary, large margin minimizes overfitting.
Support Vectors: Data points that the margin directly affects.
Kernel Trick: Projects non-linear data into higher dimensions to find a linear decision boundary.

Naive Bayes Classifiers

Framework: Based on Bayes' Theorem, applies conditional probability.
Naive Assumption: Assumes feature independence to simplify computation.
Application: Effective for text classification using a "bag of words" method (e.g., spam detection).
Comparison with Deep Learning: Faster and more memory efficient than recurrent neural networks for text data, though less precise in complex document understanding.

Choosing an Algorithm

Assessment: Evaluate based on data type, memory constraints, and processing needs.
Implementation Strategy: Apply multiple algorithms and select the best-performing model using evaluation metrics.

Links

Andrew Ng Week 7
Pros/cons table for algos
Sci-Kit Learn's decision tree for algorithm selection.
Machine Learning with R book for SVMs and Naive Bayes.
"Mathematical Decision-Making" great courses series for Bayesian methods.

63 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Learn #Machining

MLG 013 Shallow Algos 2

Machine Learning Guide

480 subscribers

published 8+ y ago

MP3•Episode home

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/13

Support Vector Machines (SVM)

Purpose: Classification and regression.
Mechanism: Establishes decision boundaries with maximum margin.
Margin: The thickness of the decision boundary, large margin minimizes overfitting.
Support Vectors: Data points that the margin directly affects.
Kernel Trick: Projects non-linear data into higher dimensions to find a linear decision boundary.

Naive Bayes Classifiers

Framework: Based on Bayes' Theorem, applies conditional probability.
Naive Assumption: Assumes feature independence to simplify computation.
Application: Effective for text classification using a "bag of words" method (e.g., spam detection).
Comparison with Deep Learning: Faster and more memory efficient than recurrent neural networks for text data, though less precise in complex document understanding.

Choosing an Algorithm

Assessment: Evaluate based on data type, memory constraints, and processing needs.
Implementation Strategy: Apply multiple algorithms and select the best-performing model using evaluation metrics.

Links

Andrew Ng Week 7
Pros/cons table for algos
Sci-Kit Learn's decision tree for algorithm selection.
Machine Learning with R book for SVMs and Naive Bayes.
"Mathematical Decision-Making" great courses series for Bayesian methods.

63 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Learn #Machining

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