English

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.

By the end of the course, students gain knowledge of the fundamental principles of heredity; they are familiar with the nature, transmission, expression, and variability of genetic information and are able to rigorously interpret genetic experimental data. The course content is divided as follows:

• Introduction to genetics
• Mendelian Genetics
  • Gregor Mendel and basic principles of heredity; monohybrid crosses (dominance and segregation); dihybrid crosses (independent assortment)
  • Predicting the outcome of genetic crosses; probability and Chi-Square test
• Cell division, mitosis and meiosis, chromosomal basis of inheritance
  • Sexual reproduction, mitosis and meiosis. Chromosome theory of inheritance
  • Sex determination and sex-linked inheritance
  • Dosage compensation in mammals
• Human pedigree analysis
• Extensions and modifications of basic mendelian principles
  • Allelic variation and gene function; why some alleles are dominant and other recessive; types of dominance, reduced penetrance, lethal alleles.
  • Genetic heterogeneity, gene interactions and epistasis.
• Linkage, recombination, gene mapping in Eukaryotes
  • Linked genes and crossing over; Constructing genetic maps with recombination frequencies
  • Linkage analysis in human; DNA polymorphisms as genetic markers; the lod-score method
• Overview of genetics of bacterial and viral genetic systems
• Genetic variation, DNA repair and recombination
  • Genetic variability; mutation and polymorphisms; types of genetic variants; molecular basis of mutations; point mutations and their consequences; mutagenesis
  • DNA repair mechanisms; DNA recombination mechanisms
  • Variation in chromosome number and structure; mechanisms of structural variation
• Population genetics
  • Variation in populations; the Hardy-Weinberg equilibrium
  • Factors that alter allele and genotype frequencies in populations
• Overview of basic techniques in molecular genetics and genomics
  • Basic techniques used to identify, amplify, clone and sequence genes; DNA libraries; genetic, cytogenetic and physical maps
  • The Human Genome Project; map based cloning of genes; association and linkage disequilibrium
  • Analyzing genomic variation
• Introduction to complex traits
  • Heritability
  • Mapping complex traits

The course also includes LABORATORY practicals:

• Genomic DNA extraction from buccal swab cells
• PCR amplification  
• SNP Genotyping  by restriction enzyme digestion

This course covers the derivation and analysis of fundamental partial differential equations, including Laplace’s equation, the wave equation, and the diffusion equation. Emphasis is placed on analytical solution techniques such as separation of variables, Fourier series and integrals, and the method of characteristics. Additional topics include maximum principles and the use of Green’s functions for solving boundary value problems.

This course focuses on the psychology of emotional well-being as well as strategies on how the study of emotion can be applied to different domains of life. It discusses various concepts related to emotional well-being from different psychological perspectives, developing a deeper understanding of the positive and negative emotions experienced by human beings and analyze their causes and effects on well-being. It explores the various models of emotion and critically analyzes their merits and limitations, as well as their practical application. It explores various classic theories such as Basic Emotion Theory, Social Constructivist Theory, Cognitive Appraisal Theory, and the RULER Model of Emotion Intelligence.

By examining the origins, scale, and evolution of the creator economy, students see how digital content, platforms, and societal shifts converge to create new economic models. Studying platform dynamics reveals how network effects, algorithmic curation, and open infrastructures drive value creation and reshape distribution. Coverage of monetization strategies and personal branding highlights the balance between revenue and authenticity, equipping students to develop sustainable creator ventures. Exploring brand–creator collaborations and the economics of influence shows how influencer tiers, pricing benchmarks, and engagement metrics inform strategic decisions. Matching platforms and analyzing the creator economy’s disruption of traditional industries illustrate the competitive landscape and strategic repositioning required in sectors like fashion and media. Legal and regulatory challenges provide insight into compliance, intellectual property, and content moderation, essential for responsible practice. Finally, surveying emerging trends such as AI and Web3 encourages foresight on innovation, ethics, and decentralization. Overall, this course bridges economics, marketing, technology, and law to develop strategic decision-making skills, foster adaptability, and prepare graduates to navigate and shape the evolving creator-driven marketplace.