Artificial Intelligence - Easily Explained For Beginners

🤖 Artificial Intelligence - Easily Explained For Beginners 🚀

Course Headline: Fundamentals of agent and multi-agent systems, neural networks, deep learning, machine learning & computer vision

Course Description:

Embark on a thrilling journey through the world of Artificial Intelligence (AI) with our beginner-friendly online course. This comprehensive video series is meticulously crafted to introduce you to the intriguing history and foundational concepts of AI, guiding you from its inception to the cutting-edge techniques of today.

What You'll Learn:

I. Introduction and Historical Background of AI 📚

• What is AI? - Explore the philosophical aspects of artificial intelligence and understand its place in our world.
• Strong vs Weak AI - Differentiate between the two types of AI and what each represents.
• The Turing Test - Learn about this iconic test and its significance in evaluating AI systems.
• The Birth of AI - Trace back to the origins of AI and understand the initial goals and expectations.
• Era of Great Expectations vs Catching up with Reality - Delve into the shifts in public perception as AI evolved.
• Teaching Machines to Learn - Discover how the concept of teaching machines led to advancements in AI.
• Distributed Systems in AI - Examine the role of distributed systems in AI's development.
• Deep Learning, Machine Learning, NLP - Familiarize yourself with these key components that shape modern AI.

II. The General Problem Solver 🧠

• Proof Program - Logical Theorist - Explore the early attempts at creating an intelligent machine.
• Example from "Human Problem Solving" (Simon) - Analyze a famous case study that shaped AI research.
• The Structure of a Problem - Learn how problems are structured and solved in AI systems.

III. Expert Systems 🧪

• Factual Knowledge & Heuristic Knowledge - Understand the two types of knowledge that expert systems use.
• Frames, Slots, and Fillers - Learn about these foundational concepts in AI.
• Forward & Backward Chaining - Discover the reasoning techniques used by expert systems.
• The MYCIN Programme - Study an early and influential example of a medical expert system.
• Probabilities in Expert Systems - Explore how probability plays a role in decision-making within AI.
• Example - Probability of Hairline Cracks - A practical case study to solidify your understanding.

IV. Neuronal Networks 🎩

• The Human Neuron - Gain insights into the neural structure of our brains.
• Signal Processing of a Neuron - Learn how neurons process information.
• The Perceptron - Explore the early model of a neural network and its limitations.

V. Machine Learning (Deep Learning & Computer Vision) 📈

• Example - Potato Harvest - A real-world application of machine learning.
• Birth Year of Deep Learning - Trace back to the year when deep learning truly took off.
• Layers of Deep Learning Networks - Dive into the architecture of these complex networks.
• Machine Vision / Computer Vision - Understand how AI can interpret and understand visual data.
• Convolutional Neural Network - Learn about this specific type of neural network that has revolutionized computer vision.

VI. Agent and Multi-Agent Systems 🤝

• Idea of an Agent and Interaction in a Multi-Agent System - Understand how agents interact with each other to perform complex tasks.
• Distributed Complexity - Discover the benefits of distributing tasks across multiple agents.

By the end of this course, you'll have a solid understanding of the historical context and the technical underpinnings of AI. Whether you're a curious beginner or an aspiring AI specialist, this course will equip you with the knowledge to navigate the exciting world of artificial intelligence.

Join us on this intellectual adventure and unlock the mysteries of AI with instructor Axel Mammitzsch, your expert guide through the landscape of learning. 🌟