INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Instructional goals
The course introduces students to foundational and emerging concepts in artificial intelligence (AI) with a specific focus on the technologies that have significantly influenced, or are poised to transform, the business landscape. It emphasizes a scientific reasoning paradigm, encouraging students to understand not only how these tools work but why and when they should be employed in real-world business contexts.
By the end of the course, students should be able to:
Understand how artificial intelligence can enhance internal business mechanisms, from operations to strategic planning.
Identify the short- and long-term potential of AI technologies across diverse industries.
Evaluate the suitability of various AI algorithms for specific business challenges and data contexts.
Comprehend the strategic importance and urgency for businesses to adopt AI-driven solutions in order to remain competitive and innovative.
Intended learning outcomes
Upon successful completion of the course, students will be able to:
Distinguish between key AI paradigms such as supervised, unsupervised, and reinforcement learning.
Apply basic machine learning models to practical classification, regression, and clustering problems.
Interpret the architecture and use cases of neural networks, decision trees, ensemble methods, and clustering algorithms.
Analyze real-world applications of generative models, large language models (LLMs), and multi-modal AI systems.
Critically assess business scenarios where AI can provide value, including customer behavior analysis, automation, predictive modeling, and decision support.
Design basic AI solutions with attention to their business relevance, feasibility, and potential impact.
Course Contents
The course spans twelve weeks and provides a comprehensive introduction to artificial intelligence, combining theoretical foundations with hands-on programming in Python. It begins with a review of essential programming skills and quickly moves into the core paradigms of machine learning, including supervised, unsupervised, and reinforcement learning. Students explore the practical applications of algorithms such as regression, classification, neural networks, decision trees, and clustering methods.
Midway through the course, the focus shifts to advanced techniques like ensemble methods and reinforcement learning in business contexts. The curriculum then introduces cutting-edge developments in the AI field, including large language models (LLMs), generative AI, and prompt engineering—key components behind the recent surge in AI-driven tools and platforms.
In the final weeks, students examine multimodal AI systems and intelligent agents, gaining insight into how modern AI can process diverse data types and make autonomous decisions. The course concludes with real-world applications across industries such as finance, marketing, and logistics, enabling students to connect technical concepts with strategic business impact.
Reference Books
All the class material is available on the LUISS e-learning platform. Additional resources that could be considered are reported following.
"Artificial Intelligence: Foundations of Computational Agents" – David L. Poole and Alan K. Mackworth
Building effective agents - https://www.anthropic.com/engineering/building-effective-agents
Agents SDK documentation - https://platform.openai.com/docs/guides/agents
Teaching Methods
Most lectures will integrate theoretical foundations with practical exercises. Working in teams, students will develop proof-of-concept solutions in response to a group project challenge assigned by the instructors. These solutions will be presented in class, with the goal of strengthening both their understanding of AI algorithms and their communication skills.
Assessment Method
Students will be evaluated based on two core criteria: a group project, constituting 70% of the final grade, and a final written exam, constituting the remaining 30%. Successful completion of the course requires all students to fulfill both components. Please note that group projects must be presented in person. Individuals unable to attend course lectures are responsible for reviewing all lecture slides and studying the supplementary texts specifically assigned to non-attending students. Additionally, non-attending students will be required to complete a final written exam that is significantly longer and more rigorous than the standard exam.
Thesis assignment criteria
A thesis will be assigned (upon specific request to the instructor) to students who have average grade >27/30 and who demonstrate a serious and motivated interest in the course topics.
Week 1
Students are introduced to the structure, goals, and expectations of the course. The session includes a recap of basic Python programming and the setup of the coding environment to ensure all students are equipped for upcoming hands-on activities.
Week 2
An overview of the three core machine learning paradigms—supervised, unsupervised, and reinforcement learning—is provided. Students compare regression and classification models through practical examples and guided exercises.
Week 3
The fundamentals of neural networks are explored, including how they model complex relationships in data. Students begin implementing basic neural networks and examining their strengths and limitations.
Week 4
Focus is placed on decision tree algorithms and Random Forests. The session involves building tree-based models and understanding how ensemble learning improves predictive performance and robustness.
Week 5
Students are introduced to clustering techniques such as K-means and hierarchical clustering. They apply these methods to datasets to uncover hidden patterns and discuss business applications like customer segmentation.
Week 6
The class examines ensemble methods beyond Random Forests, including boosting and bagging. Activities focus on combining multiple models to increase accuracy and reduce overfitting in real-world scenarios.
Week 7
This session deepens understanding of reinforcement learning through conceptual frameworks and simple interactive simulations. Applications in game theory, robotics, and dynamic decision-making are discussed.
Week 8
Students are introduced to Large Language Models (LLMs) and generative AI. The session includes demonstrations of text generation and explores how these models are disrupting industries such as marketing and customer service.
Week 9
The session focuses on prompt engineering, teaching students how to interact effectively with LLMs. Through trial exercises, they learn how prompt formulation influences output quality and behavior.
Week 10
Multimodal LLMs are explored, with emphasis on models that process multiple data types (e.g., text, image, audio). Students review case studies where these systems are applied in practical business contexts.
Week 11
Students examine AI applications across sectors such as finance, retail, logistics, and healthcare. The session includes analysis of real use cases and discussion of success factors in deploying AI solutions.
Week 12
The course concludes with a focus on intelligent agents—autonomous systems capable of perceiving, reasoning, and acting. Students explore how agents are integrated into modern workflows and their future potential in business environments.