Types of Artificial Intelligence Course

Lesson 1 • Historical Development of AI

Traces AI from early logic machines to modern systems. Provides chronological context that explains why current AI paradigms exist.

Lesson 2 • Core Concepts and Terminology

Introduces essential vocabulary including algorithms, models, and data. Ensures students can read technical literature and follow course discussions accurately.

Lesson 3 • Defining Artificial Intelligence

Clarifies what AI is, what it is not, and why definitions vary. Anchors the chapter by establishing shared vocabulary for all subsequent topics.

Lesson 1 • Functionality-Based Classification

Categorises AI by what it does: reactive, limited memory, theory of mind, and self-aware. Adds a second classification axis complementing capability-based views.

Lesson 2 • Domain and Application Classification

Organises AI by industry domain and task type such as vision, language, and robotics. Connects abstract taxonomy to real-world deployment contexts.

Lesson 3 • Learning Paradigm Classification

Groups AI by how systems acquire knowledge: supervised, unsupervised, reinforcement, and hybrid. Prepares students for deeper dives in later chapters.

Lesson 4 • Comparing and Applying Taxonomies

Demonstrates how multiple classification frameworks overlap and conflict. Students practice assigning real systems to multiple taxonomies simultaneously.

Lesson 1 • Narrow AI Performance Evaluation

Introduces accuracy, precision, recall, and domain-specific benchmarks. Equips students to critically assess vendor claims and research results.

Lesson 2 • Pattern Recognition Systems

Explores systems that identify regularities in images, audio, and text. Connects narrow AI to the perceptual tasks most common in industry deployments.

Lesson 3 • Characteristics of Narrow AI

Defines the structural and behavioural traits that make a system narrow. Grounds subsequent sections in a precise technical understanding of scope limitations.

Lesson 4 • Recommendation and Ranking Systems

Analyzes collaborative filtering, content-based, and hybrid recommenders. Shows how narrow AI drives engagement in commercial platforms.

Lesson 1 • Supervised Learning in Practice

Covers regression and classification with labelled data. Demonstrates how supervised learning solves the majority of commercial AI problems.

Lesson 2 • Unsupervised Learning Techniques

Examines clustering, dimensionality reduction, and density estimation. Shows how AI finds structure in data without human-labelled examples.

Lesson 3 • Transfer Learning and Pre-trained Models

Shows how knowledge from one domain accelerates learning in another. Explains why pre-trained models have become the dominant practical approach.

Lesson 4 • Reinforcement Learning Fundamentals

Introduces agents, environments, rewards, and policies. Explains how AI learns through trial and error in dynamic settings.

Lesson 1 • Generative AI for Text and Code

Examines text generation, summarisation, translation, and code synthesis. Demonstrates practical generative AI applications across professional domains.

Lesson 2 • NLP Core Concepts

Covers tokenization, parsing, semantic analysis, and language modelling. Establishes the technical vocabulary needed to understand modern language AI.

Lesson 3 • Transformer Architecture and Large Language Models

Explains attention mechanisms and the transformer design that powers LLMs. Connects architecture choices to the capabilities and limits of modern language AI.

Lesson 4 • Multimodal Generative AI

Covers systems that generate or interpret images, audio, and video alongside text. Extends generative AI understanding beyond language-only models.

Lesson 1 • 3D Vision and Spatial Perception

Introduces depth estimation, point clouds, and 3D reconstruction. Prepares students to understand robotics and augmented reality vision pipelines.

Lesson 2 • Object Detection and Segmentation

Examines bounding box detection, semantic segmentation, and instance segmentation. Connects vision AI to real-world tasks like autonomous driving and medical imaging.

Lesson 3 • Image Processing Fundamentals

Covers pixel representation, colour spaces, and classical image operations. Provides the signal-processing foundation underlying all modern vision AI.

Lesson 4 • Video and Temporal Vision AI

Extends image understanding to motion, tracking, and activity recognition. Shows how temporal context changes vision AI design and evaluation.

Lesson 1 • Robotics and AI Integration

Defines embodied AI and explains how perception, planning, and actuation connect. Sets the conceptual foundation for understanding autonomous physical systems.

Lesson 2 • Manipulation and Dexterous Robotics

Examines grasping, assembly, and fine motor control in robotic arms. Connects AI learning methods to physical manipulation challenges in industry.

Lesson 3 • Autonomous Navigation and Path Planning

Covers localisation, mapping, and route optimisation for mobile systems. Explains how autonomous vehicles and drones navigate complex environments.

Lesson 4 • Autonomous Vehicles and Drones

Analyzes perception stacks, decision layers, and safety systems in self-driving platforms. Illustrates the highest-stakes deployment of embodied AI today.

Lesson 1 • Strategic Implications for Organisations

Translates AGI research trends into organisational planning and competitive strategy. Enables professionals to make informed decisions about long-horizon AI investments.

Lesson 2 • AI Safety and Alignment

Examines value alignment, reward hacking, and corrigibility as core safety problems. Prepares students to understand why safety research is central to advanced AI development.

Lesson 3 • Superintelligence Scenarios and Risk

Analyzes fast takeoff, slow takeoff, and multipolar scenarios for advanced AI. Equips students to critically evaluate existential risk arguments and counterarguments.

Lesson 4 • Defining and Debating AGI

Surveys competing definitions of AGI and the criteria researchers use to assess progress. Establishes a rigorous basis for evaluating AGI claims in media and research.