This beginner-friendly video course offers a thorough introduction to the foundational concepts of artificial intelligence (AI). Covering agent systems, neural networks, deep learning, machine learning, and computer vision, the course provides a historical perspective to help learners understand the evolution of AI. It begins with a section titled "Introduction and Historical Background of AI."

Course Topics and Lessons:

I. Introduction and Historical Background

Philosophical perspective: What is AI?
Strong AI vs. Weak AI
The Turing Test
The origins of AI
The era of high expectations and its reality check
How machines learn
Distributed systems in AI
Deep Learning, Machine Learning, and Natural Language Processing

II. The General Problem Solver

Logical Theorist: The first proof program
Human problem-solving examples (Simon)
Analyzing the structure of a problem
This section explores the initial AI techniques, focusing on key concepts and groundbreaking systems that shaped early optimism in the field.

III. Expert Systems

Factual and heuristic knowledge
Frames, slots, and fillers
Forward and backward chaining
The MYCIN program
Probabilities in expert systems
Real-world application: Calculating the probability of hairline cracks
Here, you’ll learn about expert systems that address specific problems using vast rule-based knowledge bases.

IV. Neural Networks

Understanding the human neuron
Neuron signal processing
The perceptron
This section revisits the idea of replicating the human brain’s structure, introducing neural networks as a means of digital information processing. The lessons cover early approaches and the missing links that eventually led to breakthroughs in neural network development.

V. Machine Learning: Deep Learning & Computer Vision

Case study: Optimizing potato harvesting
The birth of deep learning
Layers in deep learning networks
Machine vision and computer vision
Convolutional Neural Networks (CNNs)

This section highlights the concept of agents in multi-agent systems, emphasizing their role in distributing complexity. It also delves into the advances in neural networks, machine learning, and real-world AI applications such as speech and image recognition.

Who This Course is For:

Anyone curious about artificial intelligence and its basics
Students, researchers, beginners, and advanced learners in AI

Requirements:

No prior knowledge of AI required
All topics are explained clearly and in detail

What You’ll Learn:

• The structure and design of modern AI systems
• Differences between strong and weak AI
• Fundamentals of deep learning and machine learning
• Problem structures and solving techniques
• Forward and backward chaining in AI logic
• Probabilities in expert systems
• The function of human neurons and their digital equivalents
• Layers of deep learning networks
• Machine vision and computer vision basics