The Basics of Probability and Conditional Probability

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Full notes at  ocdevel.com/mlg/13 

• 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.

• 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.

• 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.

• 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.