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Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications

Welcome to the comprehensive intelligent textbook on Machine Learning, covering fundamental algorithms and practical applications.

Course Overview

This textbook provides a rigorous yet accessible introduction to machine learning, designed for college undergraduate students with backgrounds in linear algebra, calculus, and programming. The course explores both theoretical foundations and practical implementations of essential machine learning methods.

What You'll Learn

Supervised Learning

  • K-Nearest Neighbors: Distance-based classification and regression
  • Decision Trees: Interpretable models for classification and regression
  • Logistic Regression: Probabilistic classification methods
  • Support Vector Machines: Margin maximization and kernel methods

Unsupervised Learning

  • K-Means Clustering: Discovering patterns in unlabeled data

Deep Learning

  • Neural Networks: Backpropagation and gradient descent optimization
  • Convolutional Neural Networks: Computer vision and spatial feature learning
  • Transfer Learning: Adapting pre-trained models to new tasks

Practical Skills

  • Model evaluation and validation
  • Data preprocessing and feature engineering
  • Hyperparameter tuning and optimization
  • Real-world implementation with Python (scikit-learn, TensorFlow/PyTorch)

Learning Approach

Each chapter includes: - Mathematical derivations and theoretical foundations - Algorithmic pseudocode - Implementation exercises in Python - Real-world case studies - Interactive visualizations and simulations

Prerequisites

  • Linear Algebra: Matrix operations, eigenvalues/eigenvectors
  • Calculus: Derivatives, chain rule, gradients
  • Programming: Python experience recommended

Course Structure

This intelligent textbook uses a learning graph to track concept dependencies and recommend optimal learning paths based on your current knowledge and goals.

View the Learning Graph →

Getting Started

  1. Review the Course Description
  2. Explore the Learning Graph
  3. Begin with foundational concepts
  4. Progress through supervised learning algorithms
  5. Advance to neural networks and deep learning
  6. Complete hands-on projects and applications

License: CC BY-NC-SA 4.0 DEED

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