Decision Trees: Induction and Pruning

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Page 2 of 4

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Model Selection Criteria: AIC, BIC, Cp

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

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Supervised Learning: Regression Methods

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

Decision Trees: Induction & Attributes

Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.

Generalized Linear Regression: Classification

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

Machine Learning Basics: Supervised Learning

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

Decision Forests: Structure and Training

Covers decision forests, training, weak learners, entropy, boosting, 3D pose estimation, and practical applications.