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Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Supervised Learning Essentials

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

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Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Linear Regression: Basics

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Regression Trees and Ensemble Methods in Machine Learning

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