• Knowledge of the main phases characterizing a data science and machine learning process for a real-life engineering application. • Knowledge of the major data mining algorithms for classification, regression, clustering, and association rule mining. • Knowledge of the major machine learning and deep learning algorithms. • Knowledge of the Python language. • Knowledge of the major data mining and machine learning libraries. • Ability to design, implement and evaluate a data science process. • Ability to design, implement and evaluate a machine learning and deep learning algorithm. • Ability to use and tune data mining and machine learning algorithms. • Students will acquire all the above abilities by exploiting state-of-the-art machine learning algorithms in engineering applications.

Basic programming skills.

• Data science process: main phases (0.2 cfu) • Data collection, cleaning, transformation and enrichment, and feature engineering (0.3 cfu) • Data mining algorithms: classification and clustering (0.8 cfu) • Machine learning and deep learning algorithms (0.6 cfu) • Introduction to Python and data mining and machine learning libraries (e.g., scikit-learn) (1.0 cfu) • Case study analysis (0.6 cfu) • Design in the lab of data science process and machine learning and deep learning algorithms for engineering applications (2.5 cfu)

The course includes lectures and practices on the course topics, particularly on data science process design, data preprocessing, and machine learning and deep learning algorithms (3.5 CFU). The course includes laboratory sessions on the data science processes and machine learning algorithms for engineering applications (2.5 CFU). Laboratory sessions allow experimental activities on the most widespread tools and libraries. Students will prepare a written report on a group project assigned during the course and discuss it with the professor.

Copies of the slides used during the lectures, examples of written exams and exercises, and manuals for the activities in the laboratory will be made available. All teaching material is downloadable from the course website or the teaching Portal. Book (only a few chapters needed) - Tan, Steinbach and Karpatne, Kumar; 'Introduction to data mining', 2 ed., Pearson, 2019 - Bellini, Alessandro and Guidi, Andrea; ‘Python e machine learning’, McGraw-Hill Education, 2022 - E. Matthes. Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming. No Starch Press, 2019. isbn: 9781593279288 - Jake VanderPlas. 2016. Python Data Science Handbook: Essential Tools for Working with Data (1st. ed.). O’Reilly Media, Inc. - McKinney Wes. 2017. Python for data analysis (2nd. ed.). O’Reilly Media, Inc.

Lecture slides; Text book; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions; Video lectures (current year); Self-assessment tools;

Exam: Group project; Computer-based written test in class using POLITO platform;

The exam includes a group project and a written part. The final score is defined by considering both the evaluation of the group project and the written part. The teacher may request an integrative test to confirm the obtained evaluation. Learning objectives assessment The written part will assess - the knowledge of the data preparation techniques and the major data mining algorithms for classification, regression, clustering, and association rule mining. - the knowledge of the machine learning and deep learning for engineering applications. The group project will assess - the ability to design, implement and evaluate a complete data science process, including the evaluation and tuning of machine learning algorithms and result assessment for a specific engineering application. - the working knowledge of the Python language and the major data mining and machine learning libraries. Exam structure and grading criteria The group project consists in designing and implementing a data science process, based on machine learning algorithms, for solving a data analytics task related to a specific engineering application. The project is assigned after 6 weeks of lectures. The evaluation of the group project is based on the performance and accuracy of the proposed solution, in terms of standard quality measures (e.g., prediction accuracy), and completeness (i.e., in depth analysis of each phase of the designed process and motivation for selecting given techniques and algorithms). The written part covers the theoretical part of the course. It includes multiple choice and box-to-fill questions related to the theoretical part of the course (data cleaning, data pre-processing, data mining algorithms, data science process, machine learning, and deep learning algorithms). For multiple-choice questions, wrong answers are penalized. The score of each question will be specified in the exam text. The written exam lasts 60 minutes. Textbooks, notes, and electronic devices of any kind are not allowed. The maximum grade for the group project is 20. The maximum grade for the written part is 12. The final grade is given by the sum of the two parts. The exam is passed if the grade of the group project is greater than or equal to 12, the grade of the written part is greater than or equal to 6, and the overall grade is greater than or equal to 18. If the final score is strictly greater than 31, the registered score will be 30 with honor.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.