麻豆社

Argomenc dl curs
This course offers a comprehensive introduction to the fundamental concepts, techniques, and algorithms of machine learning, as well as some platforms commonly used in practice. Students will explore introductory topics such as data preprocessing, followed by key methods in supervised and unsupervised learning, such as regression, classification, clustering and association rule mining. Moreover, Artificial Neural Networks are covered through the study of the perceptron and the multi-layer perceptron. An overview of deep networks and multi-task deep learning is provided. Foundational ideas, principles and applications of Reinforcement Learning are also covered. Throughout the course, students will not only develop a solid understanding of the theoretical underpinnings of these algorithms but also acquire practical skills in implementing data workflows, applying machine learning methods to real-world data, and evaluating model performance. Applications across diverse domains are discussed to illustrate the impact and versatility of machine learning.

Modalit茅 de ensegnament
Frontal lectures, lab assignments, project work.

Obietifs formatifs
The course belongs to the type "caratterizzanti 鈥� discipline informatiche" in the curricula 鈥淒ata Analytics鈥� and 鈥淒ata Management鈥�. This course offers a comprehensive introduction to the core concepts, techniques, and algorithms of machine learning, as well as some platforms commonly used in practice. Knowledge and understanding: 鈥� D1.1 - Knowledge of the key concepts and technologies of data science disciplines 鈥� D1.7 - Knowledge of artificial intelligence techniques and methods for the implementation of intelligent systems Applying knowledge and understanding: 鈥� D2.1 - Practical application and evaluation of tools and techniques in the field of data science 鈥� D2.2 - Ability to address and solve a problem using scientific methods 鈥� D2.6 - Ability to apply innovative techniques of data mining and machine learning to extract knowledge from complex and heterogeneous data Making judgments 鈥� D3.2 - Ability to autonomously select the documentation (in the form of books, web, magazines, etc.) needed to keep up to date in a given sector Communication skills 鈥� D4.1 - Ability to use English at an advanced level with particular reference to disciplinary terminology 鈥� D4.3 - Ability to structure and draft scientific and technical documentation

Sort de ejam
- A project, which consists in applying machine learning algorithms to real-world data, describing the algorithms and the adopted workflow, and presenting the results of an experimental analysis. - A final oral exam with questions on the content of the course.

Criters de valutazion
Evaluation criteria 鈥� Project: 50% of the mark 鈥� Final oral exam: 50% of the mark. Important note: both project and exam are required to be passed. Criteria for awarding marks Oral exam: ability to present and explain machine learning concepts, methods and algorithms. ability to select appropriate solutions for machine learning problems. Project: ability to implement data workflow to apply machine learning algorithms to real-world problems, correctness and clarity of the solution, experimental results, ability to solve machine learning problems with the appropriate technique.

Bibliografia obligatora

Introduction to Data Mining , by Pan-Ning Tang, M. Steinbach, A. Karpatne, V. Kumar. Pearson Education Ltd (2nd Edition, 2020).

Subject Librarian: David Gebhardi, David.Gebhardi@unibz.it

Bibliografia aconsieda

Recommended books for supplementary material and reference:

Machine Learning, Tom Mitchell, McGraw Hill, 1997

Pattern Recognition and Machine Learning, by Christopher M. Bishop, Springer (2006)

Data Mining and Machine Learning: Fundamental Concepts and Algorithms, by Mohammed J. Zaki and Wagner Meira, Jr, Cambridge University Press (2nd Ed.), 2020

Neural Networks and Deep Learning, by Charu C. Aggarwal, Springer (2018)

Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press (2016)

Depl霉 informazions
Software used: - Python and Jupyter Notebook (https://jupyter.org) - KNIME (https://www.knime.com)