Artificial Intelligence - Easily Explained For Beginners
Fundamentals of agent and multi-agent systems, neural networks, deep learning, machine learning & computer vision ✅
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15.72 €
Plan de aprendizaje
| 1. Introduction to Artificial Intelligence | |
| 2. The History of Artificial Intelligence | |
| 3. General Problem Solver | |
| 4. Expert Systems | |
| 5. Neuronal Networks | |
| 6. Machine Learning |
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.
Axel Mammitzsch has been active in IT support for computer users for over 15 years. For him, explaining complicated things in simple terms is part of his daily work.
In his books and video courses he does not explain boring theory from experts for experts, but explains everything practice-oriented for beginners understandably and comprehensibly.
This kind of teaching leads to quick successes and "Aha" experiences for his course participants. In addition the fun factor in his books and video courses are also very important.
In his books and video courses he like to give his knowledge, know-how, tips and tricks in an easy way to the normal computer user. He has often experienced how participants switched from frustrated to happy in seconds by providing the right answers to his students.
This knowledge is now available to everyone in his courses.
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