Physics

GENERAL OBJECTIVES: The course is introductory and of interdisciplinary interest. During the lessons the most important ideas and methods of modern machine learning will be discussed. Different examples of application of machine-learning algorithms in several fields of physics, science in general and technology will be also illustrated. The main goal of the course is to provide the student with a solid background on the topics addressed and to enable him to represent the knowledge about a specific problem domain and use reasoning techniques in order to design mmachine learning algorithms through a correct formulation of the problem, a critical choice of the learning algorithm and an experimental analysis to evaluate the obtained results. The course includes a substantial fraction of practical lessons with the use of computer systems (slides shown in classroom) in order to allow the student to apply the general concepts to real applications. During the practical exercises some of the machine learning algorithms discussed during the lessons will be implemented and applied to real problems. The course provides useful and expendable knowledge: - industry/technology/medical related: development and application of machine learning algorithms in leading technology sectors (automatic vision systems, autonomous driving systems, industrial automation,AI systems for robotics. etc ..); in medicine (recognition and segmentation of diagnostic images, etc.); in information technology companies (big-data analysis, search engines etc.); in finance (predictive algorithms, stock markets analysis and forecasts etc.). - basic and applied research: machine learning is a tool today widely used in both theory and experimental research, for example in high energy physics (in real-time data acquisition systems (triggers), in the interpretation of experimental results in the context of different theoretical models, etc.), or in neurosciences and cognitive psychology (be able to build a machine that learns from experience could in fact provide a useful tool to understand how animals and humans learn themselves from experience, may provide an useful model to characterise the behaviour of the brain, or could help to understand the relationship between some learning algorithms (for example, reinforced learning) and animal psychology models used to describe the influence of prizes in response to behaviour aimed at achieving a goal (concepts applicable to both animal models and artificial intelligence systems). SPECIFIC OBJECTIVES: A - Knowledge and understanding OF 1) To know the fundamentals of Machine Learning methods OF 1) To know the fundamentals of Deep Learning methods based on Differntiable Neural Networks OF 3) To understand the language of ML and DL B - Application skills OF 4) To be able to assemble simple programs of supervised and non-supervised learnign OF 5) To be able to solve simple science problems using ML and DL techniques OF 6) To be able to assess performances of ML and DL trained models C - Autonomy of judgment OF 8) To be able to evaluate the best way of implementing a ML and DL model D - Communication skills OF 9) To know how to communicate in written reports the results the work OF 10) To know how to discuss the characteristics and functionalities of simple computational models based on ML and DL E - Ability to learn OF 11) Being able to consult the API of the most used ML and DL software libraries OF 12) Being able to design and delploy a simple project of Applied Artifcial Intelligence