95965 - Artificial Intelligence and Machine Learning for Business

Learning outcomes

At the end of the course the student is in possession of the basic knowledge of cutting-edge models and applications of artificial intelligence, with particular attention to machine learning. In particular, they are able to have a practical vision to select the appropriate methods to solve concrete problems

Course contents

1. Course Introduction & Enabling Technologies (Theory, 2 hours)

• From Biological Signals to Digital Data
• Python for Non-Programmers

Introduction to Artificial Intelligence (Theory, 2 hours)

• AI Definitions, applications, and Ethics
• The Machine Learning Paradigm and Taxonomy of AI
• Supervised Learning and the Overfitting Problem

Decision Support Systems (Theory, 2 hours)

• Dynamic systems, sustainability, and the decision-making process
• Classic and new generation DSS
• Operational techniques for evidence-based systems

Python for Data Science (Theory and Practice, 18 hours)

• Basics for Beginners, Data Structures & Functions
• Lab Sessions with Google Colab

Mathematical Models & Data Exploration (Theory and Practice, 14 hours)

• Numerical Computing, Data Wrangling, and Visualization
• Lab Sessions with Google Colab

Machine Learning & Business Applications (Theory and Practice, 18 hours)

• Supervised Learning: Pipeline, Classification, and Regression
• Unsupervised Learning: Clustering and Dimensionality Reduction
• Deep Learning: Basic concepts and Lab Sessions

Project Work (Practice, 4 hours)

• Supervised group laboratory for final project development

Readings/Bibliography

The teaching materials for this course are available on the Virtuale Learning Environment (https://virtuale.unibo.it/?lang=en ).

Required readings:

Course Materials: Slides, Python Notebooks, and datasets available on the Virtuale platform.

Teaching methods

The teaching method used involves the integration of methodological content with the operational aspects of real case studies of the application of Artificial Intelligence and focuses on inductive content coding techniques and the development of problem-solving algorithms. Students will work in groups to solve real-world data science problems. A specific session will cover technical reporting.

Assessment methods

The assessment of the Monodisciplinary Course consists of two parts:

1. Group Project: Submission of a technical report covering data processing, ML model implementation, and results analysis related to an assigned real-world problem.
2. Oral Exam: Group presentation of the project followed by individual questions on theoretical topics (AI, DSS, ML) and project contribution.

Grading Scale Evaluation Guidelines:

• 18–22: Basic understanding of only a few course topics; analysis and programming skills emerge only with the instructor’s help; generally correct language.
• 23–26: Limited knowledge of several topics; independent analysis and Python implementation only on basic procedural issues; correct language use.
• 27–29: Broad knowledge of course topics; ability to make autonomous critical judgments regarding data pipeline architecture; mastery of discipline-specific terminology.
• 30–30L: Comprehensive and deep understanding of course content; fully independent critical analysis, implementation optimizations, and cross-topic architectural connections; excellent command of terminology and argumentation skills.

The result of the oral exam will be communicated at the end of the session. The minimum passing grade is 18/30.

The exam is considered passed only if all parts are successfully completed. The final grade is determined by the average of the grades from the various parts of the exam, expressed out of 30. A minimum final grade of 18/30 is required.

Negative results are not graded numerically but recorded as “withdrawn” or “failed” in the electronic transcript on AlmaEsami, and do not affect the student’s academic record.

Grades for individual parts and the final grade will be published on the AlmaEsami platform (https://almaesami.unibo.it/?lang=en ) within 5 working days of the date of the exam.

Students may reject the final grade 2 times, by informing the course examiner via email within 5 working days.

The designated course contact for this course is idio.guarino@unibo.it

Students can register for exams through the AlmaEsami platform (http://almaesami.unibo.it/ ). Exams are scheduled during the designated periods in the academic calendar. Additional sessions are available for students beyond the standard program duration.

Students with learning disorders and/or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

With regard to the learning assessment, limited, declared, and non-substantive use of AI is permitted for support activities, such as summarization and rephrasing. Substantive use of AI to complete parts of the assessment task is not permitted.

Teaching tools

• Lecture slides as PowerPoint presentations
• Python Programming Language and its scientific libraries (e.g., NumPy, Pandas, Matplotlib, Scikit-learn): The primary tool for data processing and machine learning.
• Google Colab and Python notebook templates.

In case of difficulty understanding the course content, the instructor is available for clarification meetings, which must be scheduled via email.

Office hours

See the website of Idio Guarino