In recent years, the convergence of Artificial Intelligence (AI) and Decision-Making processes has become a transformative force across a wide range of industries. The integration of AI technologies is reshaping entrepreneurship and management landscapes in sectors such as healthcare, finance, manufacturing, and retail in particular Supply Chain issues.
Machine learning models and advanced neural networks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), have achieved significant breakthroughs in fields like computer vision, natural language processing, and predictive analytics. Their capabilities extend to practical applications in healthcare, business, and autonomous vehicles, where they excel in analyzing and leveraging complex data. However, the intricate architectures and inherent opacity of deep learning neural networks and supervised machine learning pose challenges to their comprehensive understanding and limit their application in critical areas, particularly in interdisciplinary domains. To tackle this issue and expand the scope of research, by this course we are broadening the focus of our mission to encompass successful applications of Machine Learning and Deep Learning Neural Networks. This expansion aims to showcase their effectiveness in extracting valuable insights from complex datasets, thereby enhancing understanding and application across various contexts.
Course Main Topics:
Explainable Artificial intelligence;
Mathematical modeling and forecasting;
Machine and deep learning;
Nonlinear programming and AI;
Decision support Systems.
Mathematical Tools, Structures on Vector Spaces, Linear Algebra, Statistics and Informatics.
Lectures, Laboratory activities at the Decision LAB, Seminars and Workshops
Written and oral examination
Evaluation criteria:
30 cum laude: complete, in-depth and critical knowledge of the topics, excellent language skills, complete and original interpretative ability, full ability to independently apply knowledge to solve the proposed problems;
28 - 30: complete and in-depth knowledge of the topics, excellent language skills, complete and effective interpretative skills, able to independently apply the knowledge to solve the proposed problems;
24 - 27: knowledge of the topics with a good degree of mastery, good command of the language, correct and sure interpretative ability, good ability to correctly apply most of the knowledge to solve the proposed problems;
20 - 23: adequate knowledge of the topics but limited mastery of the same, satisfactory language skills, correct interpretative ability, more than sufficient ability to independently apply the knowledge to solve the proposed problems;
18 - 19: basic knowledge of the main topics, basic knowledge of the technical language, sufficient interpretative ability, sufficient ability to apply the acquired basic knowledge;
Massimiliano Ferrara: "Lectures Notes on Machine Learning and related tools". MIMEO, Reggio Calabria, 2024
Massimiliano Ferrara: Explainable artificial intelligence and mathematics: new frontiers (and challenges) of research not only as "AppliedMath", International Journal of Mathematical Analysis, Vol. 18, 2024, no. 1, 11-19
Tiziana Ciano and Massimiliano Ferrara: Karush-Kuhn-Tucker conditions and Lagrangian approach for improving machine learning techniques: A survey and new developments, AAPP, Vol 102, n.1, 2024
Further reading (for more):
Stuart Russel, Peter Norving (a cura di Francesco Amigoni): "Intelligenza Artificiale" Volume 2, Quarta edizione. PEARSON 2022 (Chapters from 19 to 24, 27-28)
Contents
Chapter 1. Statistical Learning Theory
Chapter 2. Local Methods
Chapter 3. Bias Variance and Cross-Validation
Chapter 4. Regularized Least Squares
Chapter 5. Regularized Least Squares Classification
Chapter 6. Feature, Kernels and Representer Theorem
Chapter 7. Regularization Networks
Chapter 8. Logistic Regression
Chapter 9. From Perceptron to SVM
Chapter 10. Dimensionality Reduction
Chapter 11. Variable Selection
Chapter 12. Density Estimation & Related Problems
Chapter 13. Clustering Algorithms
Chapter 14. Graph Regularization
Chapter 15. Bayesian Learning
Chapter 16. Neural Networks
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