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IB
A4 Machine learning

IB Computer Science Topic A4: Machine Learning

A1.1 Computer hardware and operation

A1.2 Data representation and computer logic

A1.3 Operating systems and control systems

A1.4 Translation (HL only)

A2.1 Network fundamentals

A2.2 Network architecture

A2.3 Data transmissions

A2.4 Network security

A3.1 Database fundamentals

A3.2 Database design

A3.3 Database programming

A3.4 Alternative databases and data warehouses (HL only)

A4.1 Machine learning fundamentals

A4.2 Data preprocessing (HL only)

A4.3 Machine learning approaches (HL only)

A4.4 Ethical considerations

B1.1 Approaches to computational thinking

B2.1 Programming fundamentals

B2.2 Data structures

B2.3 Programming constructs

B2.4 Programming algorithms

B2.5 File processing

B3.1 Fundamentals of OOP for a single class

B3.2 Fundamentals of OOP for multiple classes (HL only)

B4.1 Fundamentals of ADTs

IB Computer Science Topic A4 Machine Learning (SL/HL) covers syllabus content. Use these Notes, Questionbank, Flashcards, and Lessons to review the topic, practise exam questions, and move between notes, videos, flashcards, and lessons where available.

Practice

Question Bank

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250 questions

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Study Guide

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47 chapters

Lessons

Step-by-step lessons and quizzes

21 lessons

Flashcards

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17 flashcard decks

Reference

Key Definitions

Essential terms and concepts explained

80 definitions

A4.1.1 Types of Machine Learning and Their Applications in the Real World

A4.1.2 Hardware Requirements for Various Scenarios Where Machine Learning is Deployed

A4.2.1 Significance of Data Cleaning (HL only)

A4.2.2 Role of Feature Selection (HL only)

A4.2.3 Importance of Dimensionality Reduction (HL only)

A4.3.1 Linear Regression in Predicting Continuous Outcomes (HL only)

A4.3.2 Classification Techniques in Supervised Learning (HL only)

A4.3.3 Role of Hyperparameter Tuning in Supervised Learning Algorithms (HL only)

A4.3.4 Clustering Techniques in Unsupervised Learning (HL only)

A4.3.5 Learning Techniques Using the Association Rule Used in Large Data Sets (HL only)

A4.3.6 Agents Learning to Make Decisions (HL only)

A4.3.7 Application of Genetic Algorithms (HL only)

A4.3.8 Structure and Function of ANNs (HL only)

A4.3.9 Design of CNNs (HL only)

A4.3.10 Model Selection and Comparison in Machine Learning (HL only)

A4.4.1 Ethical Implications of Machine Learning in Real-World Scenarios

A4.4.2 Ethical Aspects of the Increasing Integration of Computer Technologies into Daily Life

What this topic covers