“Getting Started with AI: Foundations for Students” is an introductory and interactive course designed to provide a clear overview of the main concepts behind Artificial Intelligence and Machine Learning.
Getting Started with AI: Foundations for Students
Training details
Location
Online
Start Date
04/11/2026
Time
09 : 00
End Date
04/11/2026
Target Audiance
Non-Technical
Teaching language(s)
Romanian
Organizing institution
ICI Bucharest
Delivery mode
Online
Format
Case Study Session, Lecture
Topics / Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Neural Networks, AI Ethics, AI Applications
What You Will Learn
After attending this session, participants will be able to:
- Understand the main concepts behind Artificial Intelligence, Machine Learning and Deep Learning.
- Recognize common AI applications in everyday life.
- Understand the role of data, features and algorithms in Machine Learning.
- Distinguish between supervised and unsupervised learning.
- Understand the basic principles of regression and classification.
- Become familiar with clustering and other Machine Learning techniques.
- Understand the basic concepts of Reinforcement Learning and ensemble methods.
- Understand how neural networks and Deep Learning models work at a conceptual level.
- Identify common limitations, errors and biases in AI systems.
- Explore how AI can be used as a learning and professional development tool.
- Apply basic AI concepts through practical scenarios, quizzes and team exercises.
Agenda
| 09:00 – 10:00 | Introduction to Artificial Intelligence
Overview of AI, Machine Learning and Deep Learning, including everyday applications, the role of data and algorithms, common limitations and popular myths about AI. |
| 10:00 – 12:00 | Supervised Learning: Regression & Classification
Introduction to supervised learning, covering regression and classification methods, model evaluation and practical examples of how Machine Learning models make predictions. |
| 12:00 – 13:00 | Practical Machine Learning Exercise
Hands-on activity in which participants work with a prepared dataset, identify features and target variables, train a simple model and analyze its predictions. |
| 13:00 – 14:00 | Unsupervised Learning & Complementary ML Methods
Introduction to clustering, dimensionality reduction and other unsupervised learning approaches, with practical examples and real-world applications. |
| 14:00 – 15:00 | Neural Networks & Deep Learning
Introduction to neural networks, their evolution and the basic concepts behind Deep Learning and modern AI architectures. |
| 16:00 – 17:00 | AI Scenarios, Future Skills & Final Exercise
Interactive discussion on AI applications, future skills, ethics and responsible use of AI, followed by a team exercise and final quiz. |
Course Description
Artificial Intelligence is becoming part of everyday life, from recommendation systems and digital assistants to healthcare, education and business applications. Understanding how these systems work is increasingly important, even for students without an advanced technical background.
“Getting Started with AI: Foundations for Students” is an introductory and interactive course designed to provide a clear overview of the main concepts behind Artificial Intelligence and Machine Learning.
During the course, participants will explore supervised and unsupervised learning, Reinforcement Learning, ensemble methods, neural networks and Deep Learning. Concepts such as regression, classification and clustering will be introduced through accessible examples and practical exercises.
Participants will also discuss common AI limitations, bias and errors, as well as the impact of AI on education and the future job market.
The course combines presentations, practical exercises, quizzes and interactive discussions, with a focus on clarity, curiosity and understanding rather than advanced technical performance.