Methods for big data and machine learning
Course objectives
General targets: to acquire basic and advanced knowledge and hands-on experience on some classic topics in finite-dimensional optimization. Specific targets Knowledge and understanding: Understanding of the theoretical foundations of optimization theory and of the main algorithm classes for the solution of optimization problems. Applying knowledge and understanding: the student will be able to identify relevant characteristics of optimization problems and to select the most appropriate solution method for a given problem, also taking into account practical constraints due to the applicative environment (for example, the required accuracy or time limits). In addition the student will be able to correctly analyze the results provided by commercial or ad-hoc resolution software. Making judgements: ability to enucleate the most significant aspects of an optimization problem and of its solution methods. Communication skills: ability to enucleate the significant points of the theory, to know how to illustrate the most interesting parts with appropriate examples, to discuss mathematically the most subtle points. Learning skills: the acquired knowledge will allow the student to undertake more advanced studies in optimization and to be able to work in industrial and research environments where optimization is used.
Program - Frequency - Exams
Course program
Prerequisites
Books
Teaching mode
Frequency
Exam mode
Bibliography
Lesson mode
- Academic year2024/2025
- CourseApplied Mathematics
- CurriculumMatematica per Data Science
- Year1st year
- Semester2nd semester
- SSDMAT/09
- CFU6
- Subject areaAttività formative affini o integrative