MICROTHINGZ

This video course on artificial intelligence is aimed at beginners and is designed to teach you the basics within the historical development of AI. For this reason, our journey begins with the section "Introduction and historical background of AI".

Topics and contents of the lessons:

I. Introduction and historical background

• What is AI - a philosophical consideration
• Strong and Weak AI
• The Turing Test
• The birth of the AI
• The era of great expectations
• Catching up with reality
• How to teach a machine to learn
• Distributed systems in the AI
• Deep Learning, Machine Learning, Natural Language Processing
  
  

II. The general problem solver

• Proof Program - Logical Theorist
• Example from "Human Problem Solving" (Simon)
• The structure of a problem
  
  

In this section, we first take up the initial techniques of AI. You will learn about the concepts and famous example systems that triggered this early phase of euphoria.

III. Expert Systems

• Factual knowledge and heuristic knowledge
• Frames, Slots and Filler
• Forward and backward chaining
• The MYCIN Programme
• Probabilities in expert systems
• Example - Probability of hairline cracks
  
  

In this section, we discuss expert systems that, similar to the general problem solvers, only deal with specific problems. But instead, they use excessive rules and facts in the form of a knowledge base.

IV. Neuronal Networks

• The human neuron
• Signal processing of a neuron
• The Perceptron
  
  

This section heralds a return to the idea of being able to reproduce the human brain and thus make it accessible to digital information processing in the form of neural networks. We look at the early approaches and highlight the ideas that were still missing to help neural networks achieve a breakthrough.

V. Machine Learning (Deep Learning & Computer Vision)

• Example - potato harvest
• The birth year of Deep Learning
• Layers of deep learning networks
• Machine Vision / Computer Vision
• Convolutional Neural Network.
  
  

The idea of an agent and its interaction in a multi-agent system is described in the fifth section. The main purpose of such a system is to distribute complexity over several instances.

The sixth section deals with the breakthrough of multi-layer neural networks, machine learning, machine vision, speech recognition and some other applications of today's AI.

Who this course is for:

• People who want to get basic information about the topic of artificial intelligence.
• For interested students, researchers, beginners and advanced students in the field of artificial intelligence (AI).

Requirements:

• No prerequisites in the field of AI necessary.
• Everything is explained in detail in an understandable way.

What you'll learn:

• You will learn to understand the structure and design of modern artificial intelligence systems.
• You will learn to distinguish between strong and weak AI.
• You will learn what "Deep Learning" is.
• You will learn what "Machine Learning" is.
• What is the structure of a problem.
• You will learn about forward and backward chaining.
• Learn about probabilities in expert systems.
• You will learn about the human neuron.
• Learn about the layers in deep learning networks.
• You will learn about machine vision / computer vision.