English

This course examines how smart technologies like AI, urban digital twinning, and internet of things are reshaping urban planning, design, and decision-making processes. It explores their potential and limitations in tackling urban challenges, improving efficiency, and aligning with sustainability development goals while critically examining ethical concerns surrounding their implementation in cities.

This course delves into the theoretical underpinnings and practical applications of deep neural networks. Deep learning has revolutionized industries ranging from healthcare to finance, driving advancements in natural language processing, computer vision, and autonomous systems. 

From understanding fundamental concepts to implementing advanced architectures like convolutional and recurrent networks and transformers as well, this course covers both theoretical knowledge and hands-on experience essential for navigating the complexities of deep learning. 

Topics include Deep learning basics, Neural networks, Training neural networks, Convolutional neural networks, Recurrent neural networks, Transformers, Applications: NLP, Applications: CV, Generative models. 

This course guides students through the rich tapestry of classical music by immersing them in live piano performances accompanied by humorous commentary, fostering an understanding of music history, renowned composers, and significant classical pieces. Through lively discussions and sharing among students, individuals cultivate their unique perspectives and tastes in music, while also advocating for the profound impact of arts and humanities. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. This course builds the ability to analyze in a critical and professional way current social and economic issues that are relevant for public policy, using the tools of economic analysis and considering the political aspects involved. Through an in-depth discussion of selected current issues and applying, among the others, the conceptual tools developed during the first year of the program, students learn (i) how to structure an economic analysis of a particular public policy issue, (ii) how to perform the analysis, (iii) how to write a policy report or briefing, and (iv) how to summarize and present it both orally (without support) and in a presentation (with support), in order to convey effectively such analysis is a non-technical way to the policy maker or to public executives.

Course contents:

• Analysis and Discussion of Current Public Policy Issues: The selection of topics may change annually, depending on the evolving economic and political landscape and the emergence of new issues.
• Policy Making Process Management: Regardless of the specific topics, students develop the ability to manage the entire process: framing, implementing, and presenting a policy report or brief.
• Writing an Effective Policy Report/Brief: Techniques and strategies for crafting clear and compelling policy documents.
• Effective Presentation of the Policy Report/Brief: How to persuasively and professionally present the findings of the report/brief.
• Oral Briefing to a Policymaker: Skills for orally communicating with policymakers, summarizing, and conveying policy analysis clearly and concisely.

In today's world, data-driven science is paramount, and biology is no exception. This course delves into the principles of data-driven biology, exploring platform technologies and their applications across various domains, including genomics, transcriptomics, proteomics, interactomics, and other 'omics ' branches of biology. 

Students engage in discussions about the influence of 'omics' on human disease research and medicine. This course helps students to understand the latest trends in data-driven bio research and forecasts for the future bio industry. The course builds fundamental understanding and application skills in various omics technologies, and explores the past, present, and future of genomic medicine in relation to paradigm shifts in healthcare.  

The key topics of the course include the following: 1. Introduction of Omics and data-driven biology 2. Genome Projects 3. Next-generation sequencing technology (NGS) 4. Transcriptomics with DNA-chip and NGS 5. Proteomics with Mass Spectrometry 6. Variomics (human genetic variation, genotype-to-phenotype) 7. Pharmacogenomics 8. Epigenomics 9. Regulomics 10. Interactomics (molecular interactions) 11. Metagenomics (Microbiomics) 12. Single-cell Omics (Single Cell Transcriptomics) 13. Cancer Genomics 14. Cancer Immunogenomics. 

Prerequisites: General Biology, Biochemistry, Genetics 

This course considers how stories are told in an age of digital media. For the first half of the semester, we focus on key terms and concepts in narrative theory and practice, applying these terms to different narratives. We look at some good examples from various media and explore their narrative structures with each application. Additionally, we analyze how traditional narratives can be transformed into new forms suitable for digital media, where stories are increasingly told, mediated, and experienced via spaces. Traditional emphases on plot and characters are increasingly shifting toward the significance of ambience and spatial arrangements in digital and interactive media, marking a shift toward what is often called “spatial storytelling.” In the latter half of the semester, we learn about narrative genres. We explore formal patterns and storytelling strategies in each genre and observe the kinds of similar or different patterns that arise in genre-based digital stories. Eventually, we move beyond mere criticism and theoretical understanding by applying our narrative skillsets to creating actual digital content, albeit at a very basic level suitable for those inexperienced with content creation. In the process, students acquire some basic skills to work within new media technologies—especially very basic content creation skills using AI tools.

This course examines key concepts and principles underpinning conservation and their application to conservation practice. Drawing on real-world examples from terrestrial and marine ecosystems, the course highlights the challenges and broader impacts of biodiversity conservation. It explores questions such as: Who owns wildlife? Who are the winners and losers of conservation interventions? Does it matter if tigers go extinct? Can hunting benefit conservation?

This course develops core competencies for effective learning, wellbeing, personal growth and coping with stress, so that students can equip themselves with lifelong skills for learning, working and being well. Students develop themselves to thrive in university life and beyond - including leadership skills for future employment. This course covers concepts of managing stress, motivation, time management, critical and creative thinking, happiness, personality, positive self-identity, and most importantly fostering physical, cognitive, emotional and social skills that support  learning and wellbeing. Assignments provide students with the opportunity to focus on the self working towards personal goals that students identify as part of the module and track their own personal data and progress in areas of their choice (e.g. emotional wellbeing, study habits, time management, exercise). Students reflect on how their values and goals map onto the way they are currently living and students are facilitated in exploring how they wish to reach their potential. 

This course develops a solid understanding of reinforcement learning, a major area within machine learning and artificial intelligence. Reinforcement learning is grounded in various probabilistic and statistical theories and has recently been widely applied to the training of large-scale machine learning models. The course covers the theoretical foundations, applications, and current research trends in this field. Topics include Finite Markov Decision Processes, Dynamic Programming, Temporal Difference Learning, Eligibility Traces, Generalization and Function Approximation, On-policy Approximation of Action Values, Off-policy Approximation of Action Values and Policy Approximation, Meta/Multi-task Learning and RL, and Foundation Model and RL. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor.. The goal of this course is to provide students with theoretical as well as applied knowledge of frameworks and tools for measuring, interpreting, and improving business performance and assessing financial risk. In particular, at the end of the course students are able to: use financial statement information to analyze business performance; implement a management system based on the use of performance ratios; and design a performance measurement system to support management control tasks.
 

Course contents include: accounting, accounting history, financial accounting, accounting standards, international harmonization of accounting, and financial audit; The bookkeeping process and adjustments at the end of the accounting period; Financial statements, statement of financial position, statement of comprehensive income, statement of changes in equity, and statement of cash flows; Explanatory notes and management's discussion and analysis; Revenue recognition and inventory; Financial Instruments: Assets and Liabilities; Property, plan and equipment, investment property, intangible assets, leases.