Description
AI Basics is the basic course in Artificial Intelligence ideal for a diverse audience who wants to get an overview of the fundamental concepts of this booming topic.
It starts with a definition of the topic, with emphasis on the differences between subgroups such as Machine Learning, Deep Learning and Generative AI, and related application examples in industry.
Macro Topics
- Introduction
- Historical Introduction
- Areas of application
- When it pays to use AI
- AI for industry
- Basic definition
- Artificial Intelligence
- Definition
- When is it used?
- Data Science
- Data
- Traditional programming vs. AI algorithms
- Machine Learning
- Deep Learning
- Generative AI
- NLP
- Case studies
- Machine Learning
- Building an ML system
- Breakdown of the dataset
- ML supervised
- ML unsupervised
- Deep Learning
- Artificial Neural Networks
- Supervised and unsupervised learning
- CNN
- RNN
- Autoencoder
- Model evaluation methods
- Generalization
- Establishing the goodness of an algorithm: Bias and Variance
- Error training and testing
- Overfitting and underfitting
- Tools
Trainee’s Profile
Managers and technical figures
Pre-Requirements
None
Objectives
The course provides a basic understanding of the concepts inherent in Artificial Intelligence, Classical Machine Learning and Deep Learning in order to make it clear what the potential of these technologies is and what the difficulties in applying them are.
In particular, it aims to define when and why it may be useful to use these new technologies in industry so as to provide the necessary tools for those taking the course to understand whether AI can be integrated into their business, how, and in what way.
More info
English language available upon request.
The subscription must be carried out within the fifth working day preceding the course starting date.
