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In this course, you will learn:
- The Basic Foundations of Artificial Intelligence
- Differentiating between the various topics related to Artificial Intelligence
- What problems does a specific algorithm solve?
- The several algorithms you have available to address a specific problem
- Application of search algorithms
- Supervised application of learning algorithms
- Application of unsupervised learning algorithms
- Reinforced learning algorithms are used in this application.
- In Python, artificial intelligence libraries are used.
Syllabus:
1. Introduction
- Description of Artificial Intelligence
- History of Artificial Intelligence
- Distinction between issues surrounding Artificial Intelligence
- Applications
- Initiation to Artificial Intelligence libraries
2. Traditional Artificial Intelligence algorithms
- Knowledge-based Artificial Intelligence
- Search algorithms
- Algorithms for playing games
3. Machine learning: Supervised learning
- Introduction to supervised learning
- Prediction
- Classification
- Introduction to neural networks
4. Machine learning: unsupervised and reinforced learning
a) Unsupervised learning
- Clustering algorithms
- Dimensionality reduction
b) Reinforced
- Markov decision processes with finite state spaces
- Monte Carlo methods
- Temporary differences apprenticeships
