Schedule
The course starts from scratch and progresses with a mix of lectures and hands-on labs, building on curated datasets and use cases.
06:30 AM - 07:30 AM
AI Fundamentals
We establish a common frame of reference for AI, explore its history and evolution, introduce key concepts & terminology, share examples and Q&A
George Anadiotis
07:30 AM - 08:30 AM
Data Fundamentals
We introduce different types of data, describe their characteristics and how they're managed, processed, stored & utilized. We share examples and Q&A
George Anadiotis
08:30 AM - 09:30 AM
Data Science and Statistical Analysis
Discover how data transforms into business intelligence through the essential concepts of data science and statistics.
George Anadiotis
09:30 AM - 10:30 AM
Lab: Working with Data in Organizations
We introduce the organization, the no-code environment and the datasets we'll be using throughout the course. We explore and work with the datasets.
George Anadiotis
11:30 AM - 12:30 PM
Exploratory Data Analysis and Visualization
We transform raw data into insights and introduce Univariate, Bivariate and Multivariate analysis, Data Visualization and Hypothesis Testing
George Anadiotis
12:30 PM - 01:30 PM
Lab: Data-Driven Sales Insights
We focus on the sales dataset and explore it using the techniques we introduced: Distributions, Analysis, Visualization, Hypothesis Testing
George Anadiotis
01:30 PM - 02:30 PM
Machine Learning Fundamentals
We introduce the different types of Machine Learning, key Machine Learning concepts and algorithms
George Anadiotis
02:30 PM - 03:30 PM
Lab: Developing a Machine Learning Model for Sales Forecasting
We work on the sales dataset & use it to develop a sales forecasting model. We split the dataset, choose algorithm & features, develop, test, validate
George Anadiotis
06:30 AM - 07:30 AM
Machine Learning Algorithms & Evaluation
We introduce Machine Learning applications, evaluation and performance metrics, and more advanced algorithms
George Anadiotis
07:30 AM - 08:30 AM
Lab: Developing additional ML Models for Sales Forecasting
We continue the work on the sales dataset, using it to develop more models for sales forecasting. We try different algorithms, evaluate & compare
George Anadiotis
08:30 AM - 09:30 AM
Lab: Developing ML Models for Sales Clustering & Classification
We continue the work on the sales dataset, developing models for sales clustering and classification. We try algorithms, evaluate and compare results.
George Anadiotis
09:30 AM - 10:30 AM
Lab: Image Clustering & Classification
We work with the image dataset we introduced previously, and explore ways to extract clusters from it and develop a classifier.
George Anadiotis
11:30 AM - 12:30 PM
Natural Language Processing & Generative AI
We introduce NLP techniques to extract meaning from text, NLP applications, and Large Language Models (LLMs)
George Anadiotis
12:30 PM - 01:30 PM
Lab: Automating Meeting Admin leveraging Speech Recognition
We work with the meeting video and audio dataset we introduced, and explore ways to extract the information we need out of it by turning it to text.
George Anadiotis
01:30 PM - 02:30 PM
Lab: Refining Meeting Admin Automation leveraging GenAI Prompt Engineering
We work with meeting transcripts, using LLMs. We introduce Prompt Engineering techniques & frameworks, the use case and exercise we'll be working on
George Anadiotis
02:30 PM - 03:30 PM
Lab: Scaling up Meeting Admin Automation leveraging RAG & Knowledge Graphs
We go from working with one text file, to working with a collection of text files. We introduce RAG & Knowledge Graphs, use case, tool & exercise
George Anadiotis
04:30 PM - 07:00 PM
Convergence: Day 3 Recap, Reflection and Recreation
We reflect, discuss, and answer questions over dinner and drinks. We keep work conversation within limits, and offer optional group activities
George Anadiotis
06:30 AM - 07:30 AM
Data Governance
We introduce Data Governance practice, roles and responsibilities. We focus on Data Catalogs, and introduce Knowledge Management principles
George Anadiotis
07:30 AM - 08:30 AM
Lab: Governing Data with a Data Catalog
We learn how to work with a Data Catalog. We work in groups, simulating an organizational setting with various stakeholders.
George Anadiotis
08:30 AM - 09:30 AM
Data Modeling & Ontology Design
We introduce the fundamentals of data modeling, focusing on ontology design, and model a knowledge graph of our domain that we'll use.
George Anadiotis
09:30 AM - 10:30 AM
Lab: Problem Solving leveraging Ontology, Advanced RAG & Knowledge Graphs
We use the domain data model we developed to refine our RAG application. Then, we use the application to answer questions and solve problems.
George Anadiotis
11:30 AM - 12:30 PM
AI Integration and Deployment
We review options for AI application development, survey main AI frameworks, cloud-based services, no-code and low-code options
George Anadiotis
12:30 PM - 01:30 PM
Lab: From Model to Application
We develop and deploy a minimal application based on a machine learning model we created in one of the previous labs.
George Anadiotis
01:30 PM - 02:30 PM
AI Ethics
We introduce AI ethics for responsible AI development: bias & fairness, privacy & data protection, social & economic impact, Ethical AI frameworks
George Anadiotis
02:30 PM - 03:30 PM
AI Governance & Regulation
We introduce AI Governance & Regulation, review the regulatory landscape and compliance requirements, governance frameworks & risk management for AI
George Anadiotis
06:30 AM - 07:30 AM
AI & Business Strategy
We introduce a framework for AI strategy development & tools to document & evaluate use cases, learn cost-benefit analysis and evaluate KPIs & ROI
George Anadiotis
07:30 AM - 08:30 AM
Lab: AI Business Strategy Development
We apply the AI strategy development framework & tools we introduced in use cases built around our Organization setting, datasets & model development
George Anadiotis
08:30 AM - 09:30 AM
AI Project Management Fundamentals
We introduce key aspects of managing AI projects: the AI project lifecycle, Buy vs Build decision making, Scoping and planning AI projects
George Anadiotis
09:30 AM - 10:30 AM
Team Management & Budget in AI Projects
We learn what roles are needed in AI project teams and how to manage them, Agile methodologies in AI development, budgeting and resource allocation
George Anadiotis
11:30 AM - 12:30 PM
Lab: Capstone Project - AI Business Strategy Scale-up
We bring everything together, providing additional qualitative & qualitative context for the use cases we introduced & reevaluate initial AI strategy
George Anadiotis
12:30 PM - 01:30 PM
Lab: Capstone Project - AI Project Plan
We choose one use case to develop further, and draft a detailed project plan, budget and resource allocation, and change management plan for it
George Anadiotis
01:30 PM - 02:30 PM
Lab: Capstone Project - Presentations & Feedback
We present findings of the Capstone Project, discuss choices and assumptions, provide feedback and guidance, extrapolate to real-world use cases
George Anadiotis
02:30 PM - 03:30 PM
AI Trends and Future Directions
We introduce emerging trends in AI, discuss the future of AI in society & industry, AI-driven innovation, and share AI learning paths and resources
George Anadiotis