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.

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