Module 3: Supervised Learning - Classification Fundamentals (Weeks 6)

This section covers the transition from regression to classification in supervised learning, focusing on Support Vector Machines and Decision Trees as key classification techniques.

Module Objectives (For Week 6)

This section outlines the objectives for Week 6, focusing on key classification techniques in machine learning: Support Vector Machines and Decision Trees.

Week 6: Support Vector Machines (Svm) & Decision Trees

This section covers powerful classification techniques in machine learning: Support Vector Machines (SVM) and Decision Trees.

Support Vector Machines (Svms): Finding Optimal Separation

This section explains Support Vector Machines (SVMs), focusing on their core principles of hyperplanes, margin maximization, hard and soft margins, and the kernel trick.

Understanding Hyperplanes: The Decision Boundary

This section explores hyperplanes as decision boundaries in Support Vector Machines (SVMs) and emphasizes the importance of maximizing the margin for better classification performance.

Maximizing The Margin: The Core Principle Of Svms

This section explores the core principle of Support Vector Machines (SVMs), focusing on maximizing the margin between classes within a dataset to achieve robust classification.

Hard Margin Svm: The Ideal (And Often Unrealistic) Scenario

This section discusses the concept of hard margin Support Vector Machines (SVMs), their ideal conditions for application, and their limitations in real-world datasets.

Soft Margin Svm: Embracing Imperfection For Better Generalization

This section discusses the soft margin support vector machine (SVM) technique that balances perfect classification and generalization by allowing some misclassifications.

The Kernel Trick: Unlocking Non-Linear Separability

The Kernel Trick transforms non-linearly separable data into a higher dimensional space where linear separation is possible, significantly enhancing the power of Support Vector Machines.

Decision Trees: Intuitive Rule-Based Classification

Decision Trees are non-parametric models that classify data through a series of sequential decisions and tests based on features.

The Structure Of A Decision Tree

This section explores the fundamental structure and functioning of Decision Trees within supervised learning classification.

Building A Decision Tree: The Splitting Process

This section explains the process of constructing decision trees, focusing on the recursive splitting of nodes to maximize data purity using impurity measures.

Impurity Measures For Classification Trees

This section explores impurity measures for classification trees, focusing on Gini impurity and entropy, and their roles in guiding optimal splits during the tree construction process.

Gini Impurity

Gini Impurity is a measure used in decision trees to determine the best split during node partitions, quantifying the likelihood of misclassification for a selected class.

Entropy

Entropy is a key measure of impurity in Decision Trees, quantifying disorder or randomness within a dataset.

Overfitting In Decision Trees

Overfitting in Decision Trees occurs when the model becomes excessively complex, memorizing the training data instead of generalizing to new data, often tackled through pruning techniques.

Pruning Strategies: Taming The Tree's Growth

This section focuses on pruning strategies for Decision Trees, emphasizing the importance of reducing complexity to enhance model generalization.

Pre-Pruning (Early Stopping)

Pre-pruning, or early stopping, is a technique used in decision trees to prevent overfitting by halting the growth of the tree based on predefined criteria.

Post-Pruning (Cost-Complexity Pruning)

Post-pruning is a strategy to simplify Decision Trees by removing branches that add little predictive power, enhancing the model's generalization to unseen data.

Lab: Exploring Svms With Different Kernels And Constructing Decision Trees, Analyzing Their Decision Boundaries

This section covers the implementation and analysis of Support Vector Machines (SVMs) and Decision Trees, focusing on their decision boundaries and performance with different parameters and kernel functions.

Lab Objectives

This section outlines the objectives for the lab session focused on Support Vector Machines and Decision Trees in supervised learning.

Activities

This section details the hands-on activities related to classification algorithms, specifically Support Vector Machines (SVMs) and Decision Trees, designed to deepen practical understanding and implementation skills.

Data Preparation For Classification

This section outlines the essential steps in preparing data for classification tasks in supervised learning.

Support Vector Machines (Svm) Implementation

This section explores the foundational concepts and implementations of Support Vector Machines (SVM), emphasizing their utility in classification tasks.

Decision Tree Implementation

This section outlines the implementation and analysis of Decision Trees in classification tasks, emphasizing their construction, advantages, and the problem of overfitting.

Comprehensive Comparative Analysis And Discussion

This section explores the comprehensive comparative evaluation and discussion of Support Vector Machines (SVMs) and Decision Trees as classification techniques in machine learning.

Self-Reflection Questions For Students

This section provides students with self-reflection questions to deepen their understanding of Support Vector Machines (SVMs) and Decision Trees.