Learning Outcomes

• Identify leading technologies for AI, ML, and DL
• Distinguish hardware architectures from IoT to datacenter
• Understand key AI software platforms and frameworks
• Assess opportunities and challenges in AI adoption

Learning Content

• Week 0: Introduction
• Week 1: IT and AI hardware
• Week 2: AI on the Cloud
• Week 3: Embedded and Edge AI
• Week 4: Challenges and opportunities
• Week 5: Final quiz
• Additional resources including linkography

Learning Outcomes

• Recognize and evaluate legal implications of AI
• Understand civil and criminal liability for AI
• Distinguish between copyright and patent legal frameworks in AI
• Assess privacy and human rights risks associated with AI
• Apply AI Act principles to risk assessment and regulatory compliance

Learning Content

• Week 0: Introduction
• Week 1: Law and legal issues of AI
• Week 2: Liability for AI
• Week 3: AI and intellectual property
• Week 4: Risks to fundamental rights
• Week 5: AI Act: principles and risk classification
• Week 6: Final quiz
• Additional resources

Learning Outcomes

• Describe reasons behind the evolving big data platforms from management system perspective
• Experience various data genres and appropriate management tools for each
• Work with real-time and semi-structured data through hands-on tutorials
• Extract value from existing untapped data sources
• Discover new data sources
• Apply data modeling concepts: structures, operations, and constraints
• Work with vector space models, graph data models, and streaming data
• Understand transition from DBMS to Big Data Management Systems (BDMS)
• Use big data tools: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL, Aerospike, Solr, Lucene, Gephi

Learning Content

• Module 1: Introduction to Big Data Modeling and Management
• Module 2: Data Models
• Module 3: Additional Data Models
• Module 4: Data Formats and Streaming Data
• Module 5: Big Data Management Systems
• Module 6: Capstone Project (Catch the Pink Flamingo game data modeling)

Learning Outcomes

• Describe machine learning, deep learning, neural networks, and ML algorithms (classification, regression, clustering, dimensional reduction)
• Implement supervised and unsupervised ML models using SciPy and ScikitLearn
• Deploy ML algorithms and pipelines on Apache Spark for big data
• Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow
• Create LLMs like GPT and BERT
• Develop generative AI applications using LLMs and RAG with Hugging Face and LangChain
• Apply transfer learning, fine-tuning techniques (LoRA, QLoRA), and RLHF
• Build AI agents and QA bots with LangChain

Learning Content

• Course 1: Machine Learning with Python (20 hours)
• Course 2: Introduction to Deep Learning & Neural Networks with Keras (10 hours)
• Course 3: Deep Learning with Keras and TensorFlow (23 hours)
• Course 4: Introduction to Neural Networks and PyTorch (16 hours)
• Course 5: Deep Learning with PyTorch (20 hours)
• Course 6: AI Capstone Project with Deep Learning (14 hours)
• Course 7: Generative AI and LLMs: Architecture and Data Preparation (5 hours)
• Course 8: Gen AI Foundational Models for NLP & Language Understanding (9 hours)
• Course 9: Generative AI Language Modeling with Transformers (9 hours)
• Course 10: Generative AI Engineering and Fine-Tuning Transformers (8 hours)
• Course 11: Generative AI Advance Fine-Tuning for LLMs (9 hours)
• Course 12: Fundamentals of AI Agents Using RAG and LangChain (7 hours)
• Course 13: Project: Generative AI Applications with RAG and LangChain (9 hours)

Learning Outcomes

• Understand the process of drawing conclusions about populations from data
• Perform regression analysis, least squares, and statistical inference
• Build and apply prediction functions using machine learning methods
• Develop interactive data products and visualizations
• Use p-values, confidence intervals, and hypothesis testing
• Apply machine learning algorithms: regression trees, classification trees, Random Forests, Naive Bayes
• Understand training/test sets, overfitting, and error rates
• Create interactive maps and presentations using R tools

Learning Content

• Course 1: Statistical Inference (55 hours)
• Course 2: Regression Models (53 hours)
• Course 3: Practical Machine Learning (8 hours)
• Course 4: Developing Data Products (10 hours)
• Course 5: Data Science Capstone (5 hours)

Learning Outcomes

• Describe reasons for applying ethical analyses to AI
• Recognize, analyze, and apply key ethical frameworks to real-world AI cases
• Critically assess fairness, responsibility, and explainability in AI
• Understand the philosophical debate on Trustworthy AI

Learning Content

• Week 0: Introduction
• Week 1: Ethics, AI and Responsibility
• Week 2: Case-studies, examples and ethical frameworks
• Week 3: Issues and challenges
• Week 4: Trusting AI
• Final Quiz
• Additional resources including video transcripts and bibliography

Learning Outcomes

• Create a browser work persona and configure a productive home page
• Use task management tools (Todoist) to organize work
• Apply time tracking methods (Toggl Track) for productivity analysis
• Manage professional email and integrate CRM systems (Gmail with Streak)
• Share and collaborate on documents using cloud storage (Google Drive)
• Handle accounting and invoicing for self-employed work
• Implement focus and meditation techniques (Mynoise.net)
• Use collaborative messaging platforms (Slack) for team communication
• Conduct video calls and create virtual coworking spaces

Learning Content

• Module 1: Set up a Chrome work persona and home page
• Module 2: Set your agenda with Todoist
• Module 3: Use Toggl Track for time tracking
• Module 4: Manage your email with Gmail
• Module 5: Add a CRM to your Gmail with Streak
• Module 6: Share documents with Google Drive
• Module 7: Keep accounting and invoices for self-employed remote workers
• Module 8: Focus using Mynoise.net
• Module 9: Collaborative messaging with Slack
• Module 10: Videocalls and creating a virtual coworking space

Learning Outcomes

• Overcome neurological roadblocks to effective futures thinking
• Apply mental simulation techniques: predict the past, remember the future, hard empathy
• Create first-person future scenarios that improve strategy and motivation
• Envision multiple alternative futures simultaneously
• Lead others through first-person future simulations
• Develop cognitive flexibility and creative thinking about future possibilities

Learning Content

• Module 1: Basics of mental simulation and three unsticking techniques (Predict the Past, Remember the Future, Hard Empathy)
• Module 2: First-person futures and future self simulation
• Module 3: Alternative futures methods (simulating 4-100 versions of the future simultaneously)
• Module 4: Advanced simulation topics including VR, haptics, Afrofuturism, and experiential futures
• Practical exercises with emerging technologies (IoT, blockchain, VR, caregiving futures)

Learning Outcomes

• Explain AI origin and key concepts
• Describe major difficulties and solutions
• Summarize AI taxonomy (methods and technologies)
• Identify relevance of national AI strategies
• Recognize leading Italian and European AI organizations

Learning Content

• Week 0: Introduction
• Week 1: History of AI
• Week 2: AI Today
• Week 3: AI Research Areas
• Week 4: AI and National Strategies (USA, China, Europe)
• Week 5: Italian and European Players (MISE, CINI Labs, AIxIA, EU Networks of Excellence)
• Bibliography and additional resources

Learning Outcomes

• Practice software engineering fundamentals: test-first development, refactoring, continuous integration, and continuous delivery
• Architect and create big data or distributed systems using REST collaboration, event collaboration, and batch processing
• Create performant, scalable distributed systems that handle big data
• Compare, measure, and test big data models for production use
• Write custom performance tests to measure distributed system characteristics
• Use message queues to horizontally distribute large workloads
• Build production-ready distributed systems with monitoring and high availability

Learning Content

• Course 1: Fundamentals of Software Architecture for Big Data (20 hours)
• Course 2: Software Architecture Patterns for Big Data (25 hours)
• Course 3: Applications of Software Architecture for Big Data (17 hours)

Learning Outcomes

• Identify and evaluate different data sources for collection
• Implement effective data collection and storage strategies
• Clean and prepare raw data for analysis
• Apply statistical methods to analyze data
• Create meaningful data visualizations for decision-making
• Manage the complete data lifecycle from collection to insights
• Apply data management principles across various business contexts

Learning Content

• Module 1: Data Collection and Sources
• Module 2: Data Storage and Management
• Module 3: Data Cleaning and Preparation
• Module 4: Data Analysis Techniques
• Module 5: Data Visualization and Communication
• Module 6: Data Management Best Practices

Learning Outcomes

• Workflow of Machine Learning projects
• AI terminology
• Workflow of Data Science projects
• AI strategy

Learning Content

• What is AI?
• Building AI Projects
• Building AI In Your Company
• AI and Society

Learning Outcomes

• This course addresses users who need new skills able to embrace AI (Artificial Intelligence) potential within humanities centered analysis. Moreover, it is important to master essential digital/coding skills, to cope with the size of the information to be represented, and to couple them with the craft of effective image generation.
• It also addresses users who want to create data visualizations meant to obtain insights and readings as well as for the public communication of it, with the goal of fostering public interest about the topic.
• Furthermore, users will learn how to develop interactive narratives projects through fundamentals of Interactive Narratives, across literature and current trends with a presentation of a set of tools to support the design of interactive narrative projects for fashion and branding.

Learning Content

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Learning Outcomes

• Gain the fundamental skills needed to be a graphic designer
• Communicate through image-making and typography
• Complete a capstone project to add to your professional portfolio
• Learn everything you need to know to work in interface design, motion graphics, and editorial design
• Learn in-demand skills from university and industry experts
• Master a subject or tool with hands-on projects
• Develop a deep understanding of key concepts
• Earn a career certificate from California Institute of the Arts

Learning Content

• Fundamentals of Graphic Design - Course 1: Implement the fundamentals of color: visual, rhythm, and pattern in design; Use scale, weight, direction, texture, and space in a composition; Typeset text and experiment with letter forms; Create your own series of images using different image making techniques
• Introduction to Typography - Course 2: Review the terminology and measuring system used to describe type; Explore how typefaces tell stories and understand the historic evolution; Conduct a peer-reviewed typesetting exercise; Design of a full-scale typographic poster
• Introduction to Imagemaking - Course 3: Make informed design choices using image-based research; Create ranges of representation using images; Compose spreads for your own book; Design a book with your own images
• Ideas from the History of Graphic Design - Course 4: Learn about the history of graphic design; Understand the emergence of design as a recognized practice; Learn about graphic design radicalism in late 1950s to early 1970s; Make informed design choices
• Brand New Brand - Course 5: Synthesize typography, imagemaking, composition and systematic thinking skills through ideation, invention, and conceptualization; Demonstrate visual research and development skills through the creation of a Brand Development Guide; Expand a brand identity's palette through the inclusion of graphic marks or icons, color, secondary typefaces, and/or images

Learning Outcomes

• Understand the basics of digital twins, digital twins platform and ecosystem
• Learn the implementation of digital twins in manufacturing, the corresponding business values, and risks
• Get to know the future trends of digital twins and digital threads
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Learning Content

• What is digital twins technology? Welcome to Digital Twins! You'll learn the basics behind this technology and will be able to describe the applications and uses for digital twins within a manufacturing setting.
• Digital twins Platform, Ecosystem, and business context In our second week, we will address the digital twin platform ecosystem and the business context/advantages of digital twins. We'll also review risks and challenges surrounding this technology.
• Future Trends and Summary In our final week, you'll learn about the forecast of future trends for digital twins, learn about a related concept called digital threads, and spend time working through a case project for your final assessment.