This course provides an overview of Machine Learning (ML) methods that are meant for streaming data and that force the learner to operate in an online or incremental manner. These settings are often encountered in real-world applications, e.g., to select sponsored links for Internet advertising, or to detect frauds in credit card transaction. The online setting poses relevant challenges to classical data-driven solutions since i) the model has to integrate new pieces of information as soon as they become available, ii) the learning algorithm has to adapt to the current operating conditions, iii) the learning algorithms have to be computationally efficient, to be executed in real-time.
The course will cover classical Machine Learning techniques and present examples of their application in oncology. At first, it lays the foundations of ML and a broad description of the basic techniques in supervised, unsupervised, and reinforcement learning. Once the techniques have been presented, their application to some case studies is analysed, and a final hands-on experience will give the student the opportunity to apply ML techniques in real-world settings.
The course aims to consolidate and deepen the skills acquired in the Data Science in Healthcare course, focusing on advanced methodologies and their operational application in healthcare contexts. The program involves an in-depth study of advanced Machine Learning and Deep Learning techniques, particularly effective in processing complex data, such as images, text, and graphs.
The objective of the Machine Learning course is to give an in depth presentation of the techniques most used for pattern recognition, knowledge discovery, and data analysis/modeling. These techniques are presented both from a theoretical (i.e., statistics and information theory) perspective and a practical one (i.e., coding examples) through the descriptions of algorithms and their implementations in general purpose programming languages. At the end of this class, the students are expected to: be familiar with the most widely used techniques for pattern recognition, knowledge discovery, and data analysis/modeling; be able to write simple programs or scripts implementing them; apply them to the bionformatic analysis of biological data.
The course aims to introduce the fundamental concepts of computer science, covering the following aspects: (i) The characteristics of computer systems, their organization, and the functionalities they provide. (ii) The methodologies for defining and solving problems through an algorithmic approach. (iii) The fundamental concepts of programming, mainly using the C language, to provide solid foundations in the computational and algorithmic approach. (iv) The application of the introduced concepts through problem-solving and practical use of the programming language. (v) The transfer of the introduced concepts to a language oriented towards numerical computation, data handling, and machine learning, such as Python. (vi) The concepts and methods will be illustrated with examples in C. An introductory discussion of the Python language will be provided, covering similarities and differences between the two languages.
Politecnico di Milano, Docenza, a.a. 2019-2020, 2021-2022, 2023-2024
Università degli studi di Bergamo, Docenza, a.a. from 2017-2018 to 2019-2020
Politecnico di Milano, Esercitazioni, a.a. from 2014-2015 to 2022-2023
Politecnico di Milano, Docenza, a.a. from 2018-2019 to 2025-2026
Politecnico di Milano, Esercitazioni, a.a. from 2014-2015 to 2017-2018
Politecnico di Milano, teaching, a.a. from 2026-2027 to today
Politecnico di Milano, Docenza, a.a. from 2020-2021 to 2022-2023