Lectures related to Introduction to Supervised Learning and Decision Theory

Statistical Learning: Fundamentals

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

Supervised Learning: Formalization and Cost Functions

Covers the formalism for supervised learning and decision functions in classification problems.

Decision Theory: Risk and Inference

Explores decision theory, risk functions, and inference in statistical analysis.

Decision Theory: Risk and Hypothesis Testing

Covers decision theory, risk functions, and hypothesis testing in statistical inference.

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Statistical Theory: Decision Theory Framework

Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.

Mathematics of Data: Models and Learning

Explores models, learning paradigms, and applications in Mathematics of Data.

Image Processing II: Bayesian Classification and Decision Making

Explores Bayesian classification, decision making, and pattern recognition applications in image processing.

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Supervised Learning in Asset Pricing

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.