General Subject: Artificial Intelligence and Technolog Beginner English

Course Title: Introduction to Artificial Intelligence Subject

This course provides a comprehensive foundational overview of Artificial Intelligence (AI). Students will explore the historical context, key paradigms, core algorithms, machine learning principles, neural networks, and crucial ethical considerations driving modern AI technologies.

What you will learn:
- Define Artificial Intelligence and distinguish between symbolic AI and statistical machine learning.
- Understand and implement fundamental problem-solving search algorithms.
- Explain foundational concepts in knowledge representation and reasoning.
- Distinguish between supervised, unsupervised, and reinforcement learning paradigms.
- Grasp the basic architecture and mechanics of artificial neural networks.
- Critically evaluate algorithmic bias, AI safety, and the societal impact of intelligent systems.

Requirements:
- Basic computer literacy and introductory programming concepts.
- High school algebra and basic knowledge of probability.

This course provides a foundational overview of Artificial Intelligence, covering its history, core algorithmic search methods, knowledge representation, statistical machine learning, deep learning, and essential ethical frameworks. Students gain both theoretical understanding and practical intuition needed to explore specialized subfields of modern AI.

Course Details

Category
General
Level
Beginner
Subject
Subject: Artificial Intelligence and Technolog
Language
English
Status
Published
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Course Curriculum

Define Artificial Intelligence and trace its historical evolution. Differentiate between Rationality, Intelligence, and Human-like behavior in machines.

  • What is AI?
  • A Brief History of AI

Formulate real-world problems as state-space graphs. Compare uninformed and informed search strategies.

  • Uninformed Search Strategies
  • Informed Search and Heuristics

Express domain knowledge using formal logic. Understand how automated reasoning systems infer new information.

  • Propositional and First-Order Logic
  • Knowledge Graphs and Ontologies

Classify machine learning tasks into Supervised, Unsupervised, and Reinforcement learning. Evaluate machine learning models using appropriate metrics.

  • Supervised vs. Unsupervised Learning
  • Model Performance and Generalization

Understand the biological inspiration and mathematical formulation of artificial neurons. Identify key deep learning architectures and their application domains.

  • The Artificial Neuron and Multilayer Perceptrons
  • Deep Learning Architectures

Analyze ethical dilemmas regarding bias, privacy, and accountability in AI. Discuss current trends and future directions in artificial general intelligence research.

  • Algorithmic Bias, Fairness, and Transparency
  • Generative AI and the Path to AGI