MLG 013 Shallow Algos 2

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.