Two concepts lumped into one question, which felt a little odd.
Define both concepts clearly, then connect them to practical machine learning applications and trade-offs. Use examples to illustrate how normalization affects model training and how Bayes' theorem underpins probabilistic models.
Pro tip: Emphasize that normalization is not just about scaling but also about ensuring numerical stability and faster convergence, and that Bayes' theorem is foundational for Bayesian inference and handling uncertainty in ML.
Explain normalization as a data preprocessing technique that scales features to a standard range, such as [0,1] or mean 0 and variance 1. Mention common methods like min-max scaling and z-score normalization.
Discuss why normalization is crucial for ML algorithms, including improved convergence in gradient descent, prevention of feature dominance, and better performance for distance-based algorithms.
State Bayes' theorem mathematically: P(A|B) = P(B|A) * P(A) / P(B). Explain each term: prior, likelihood, evidence, and posterior.
Describe how Bayes' theorem is used in ML, such as in Naive Bayes classifiers, Bayesian networks, and probabilistic models for uncertainty quantification.
Highlight trade-offs: normalization may not be needed for tree-based models, and Bayes' theorem assumes independence in Naive Bayes. Mention real-world applications like spam filtering and medical diagnosis.
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