Fundamentals of ML

Fundamentals of Machine Learning

  • Author: Derbew Felasman (MSc), Debre Berhan University

Learning Objectives

  • Define Machine Learning (ML) and its significance.

  • Understand types of ML: Supervised, Unsupervised, Reinforcement Learning.

  • Recognize real-world applications of ML.

  • Explore the basic ML workflow.

  • Discuss key challenges and limitations of ML.

  • Understand relationships to other fields.

What is Machine Learning?

  • Definition: A field enabling computers to learn without explicit programming. (Arthur Samuel, 1959)

  • Key components:

    • Experience (E): Data the algorithm learns from.

    • Task (T): Problem the ML model is solved for.

    • Performance Measure (P): Metric for evaluating improvement.

ML vs. Traditional Programming

Traditional Programming

  • Characteristics:

    • Explicit instructions required.

    • Predictable output.

    • Rule-based decision making.

    • Well-defined problems.

Machine Learning

  • Characteristics:

    • Data-driven learning.

    • Probabilistic, uncertain predictions.

    • Pattern recognition in data.

    • Adaptability to new data.

Why Machine Learning Matters

  • Key Points:

    • Automates complex tasks and reduces manual work.

    • Uncovers hidden insights through data-driven decisions.

    • Drives innovation (e.g., medical diagnosis, self-driving cars).

    • Personalization of user experiences.

    • Economic impact through job creation and growth.

ML vs. Related Fields

  • Relations:

    • Artificial Intelligence (AI): Broader concept simulating human intelligence.

    • Data Science (DS): Encompasses broader processes including data collection and analysis.

    • Deep Learning (DL): Specialized ML using complex neural networks.

Types of ML Systems

  1. Supervised Learning

  2. Unsupervised Learning

  3. Reinforcement Learning

  4. Generative AI

Supervised Learning

  • Process:

    • Uses labeled input-output pairs to train algorithms.

    • Applications: Image recognition, spam filtering, weather forecasting.

  • Types of Algorithms:

    • Regression (predict continuous values) e.g., housing prices.

    • Classification (predict categorical outcomes) e.g., spam detection.

Unsupervised Learning

  • Definition:

    • Identifies patterns in data without labeled outcomes.

  • Techniques:

    • Clustering (e.g., K-Means).

    • Dimensionality reduction (e.g., PCA).

Reinforcement Learning

  • Definition:

    • Agents learn to make decisions through rewards and penalties.

  • Common Algorithms:

    • Q-Learning, Deep Q-Networks, Policy Gradient Methods.

The Machine Learning Workflow

  1. Problem Definition

  2. Data Collection

  3. Data Preprocessing

  4. Model Training

  5. Evaluation

  6. Deployment

Real-World Applications of ML

  • Sectors include:

    • Healthcare, Finance, Retail, Transportation.

  • Transforming industries—considered the new "electricity."

Challenges and Limitations of ML

  • Key Issues:

    • Data quality and bias.

    • Overfitting concerns.

    • Ethical issues (e.g., privacy, discrimination).

A Brief History of Machine Learning

  • 1950s: Neural networks and pattern recognition inception.

  • 1960s-1980s: Development of expert systems.

  • 1990s: Rise of decision trees and support vector machines.

  • 2000s-2010s: Advancements in deep learning.

Essential Math and Statistics for ML

  • Foundational Topics:

    • Linear Algebra, Calculus, Probability and Statistics.

Recap and Key Takeaways

  • Importance of ML, its types (Supervised, Unsupervised, Reinforcement).

  • Applications, workflow in ML, and ethical concerns.

  • Key Concepts: Data, Algorithms, and Models.