This Master is run under the context of Action No 2020-EU-IA-0087, co-financed by the EU CEF Telecom under GA nr. INEA/CEF/ICT/A2020/2267423
Learning Material: MSc Artificial Intelligence – University of Cyprus
- MAI611 Unit 1 – Introduction
- MAI611 Unit 2 – Problem Solving Through Search
- MAI611 Unit 3 – Constraint Satisfaction Problems, Game Playing and Planning
- MAI611 Unit 4 – Intelligent Agents and Multiagent Systems
- MAI611 Unit 5 – Knowledge Representation and Reasoning
- MAI611 Unit 6 – Knowledge Representation Formalisms of Frames and Production Rules
- MAI611 Unit 7 – Expert Systems Technology
- MAI611 Unit 8 – Deep Knowledge-Based Systems
- MAI611 Unit 9 – Knowledge Engineering
- MAI611 Unit 10 – Case-Based Reasoning and Intelligent Data Analysis
- MAI611 Unit 11 – Artificial Intelligence and Explanation
- MAI612 Unit 1 – Introduction to Machine Learning
- MAI612 Unit 2 – Data Preparation
- MAI612 Unit 3 – Regression
- MAI612 Unit 4 – Classification
- MAI612 Unit 5 – Model Evaluation and Improvement
- MAI612 Unit 6 – Trees and Forests
- MAI612 Unit 7 – Kernel-based methods 1
- MAI612 Unit 8 – Kernel-based methods 2
- MAI612 Unit 9 – Neural Networks 1: Modelling
- MAI612 Unit 10 – Neural Networks 2: Training
- MAI612 Unit 11 – Neural Networks 3: Introduction to Deep Learning
- MAI612 Unit 12 – Clustering
- MAI612 Unit 13 – Dimensionality Reduction
- MAI612 Unit 14 – Anomaly Detection
- MAI612 Unit 15 – Recommender Systems
- MAI612 Unit 16 – Introduction to Reinforcement Learning
- MAI612 Unit 17 – Markov Decision Processes and Dynamic Programming
- MAI612 Unit 18 – Model-free Reinforcement Learning
- MAI612 Unit 19 – Revision
- Course in Greek
- MAI612 Unit 1 – Introduction to Machine Learning
- MAI612 Unit 2 – Data Preparation
- MAI612 Unit 3 – Regression
- MAI612 Unit 4 – Classification
- MAI612 Unit 5 – Model Evaluation and Improvement
- MAI612 Unit 6 – Trees and Forests
- MAI612 Unit 7 – Kernel-based methods 1
- MAI612 Unit 8 – Kernel-based methods 2
- MAI612 Unit 9 – Neural Networks 1: Modelling
- MAI612 Unit 10 – Neural Networks 2: Training
- MAI612 Unit 11 – Neural Networks 3: Introduction to Deep Learning
- MAI612 Unit 12 – Clustering
- MAI612 Unit 13 – Dimensionality Reduction
- MAI612 Unit 14 – Anomaly Detection
- MAI612 Unit 15 – Recommender Systems
- MAI612 Unit 16 – Introduction to Reinforcement Learning
- MAI612 Unit 17 – Markov Decision Processes and Dynamic Programming
- MAI612 Unit 18 – Model-free Reinforcement Learning
- MAI612 Unit 19 – Revision
- MAI622 Unit 1 – Introduction
- MAI622 Unit 2 – Innovation, Research, Start-Ups
- MAI622 Unit 3 – Business Modeling
- MAI622 Unit 4 – Disciplined Entrepreneurship
- MAI622 Unit 5 – Fundraising and Pitching
- Course in German
- Course in Italian
- Course in Bulgarian
- Course in Greek
- MAI623 Unit 1 – Introduction to Natural Language Processing
- MAI623 Unit 2 – Fundamental Text Pre-Processing
- MAI623 Unit 3 – Language Modeling
- MAI623 Unit 4 – Text Classification
- MAI623 Unit 5.1 – Vector Semantics
- MAI623 Unit 5.2 – Word Vector Semantics
- MAI623 Unit 5.3 – Distributed Contextual Embeddings
- MAI623 Unit 6.1 – NLP Case Studies and Applications Hate-speech
- MAI623 Unit 6.2 – NLP Case Studies and Applications Misinformation
- MAI623 Unit 6.3 – NLP Case Studies and Applications Polarization
- MAI623 Unit 7 – Understanding Large Language Models
- Course in Greek
- MAI623 Unit 1 – Introduction to Natural Language Processing
- MAI623 Unit 2 – Fundamental Text Pre-Processing
- MAI623 Unit 3 – Language Modeling
- MAI623 Unit 4 – Text Classification
- MAI623 Unit 5.1 – Vector Semantics
- MAI623 Unit 5.2 – Word Vector Semantics
- MAI623 Unit 5.3 – Distributed Contextual Embeddings
- MAI623 Unit 6.1 – NLP Case Studies and Applications Hate-speech
- MAI623 Unit 6.2 – NLP Case Studies and Applications Misinformation
- MAI623 Unit 6.3 – NLP Case Studies and Applications Polarization
- MAI623 Unit 7 – Understanding Large Language Models
MAI 631 - Artificial Intelligence Ethics II
- MAI631 Unit 1 – The Value-Assignment Gap
- MAI631 Unit 2 – Value Sensitive Design for Socio-technical systems
- MAI631 Unit 3 – Basics of Law and Regulation of AI
- MAI631 Unit 4 – Data Governance
- MAI631 Unit 5 – Machine Ethics
- MAI631 Unit 6 – Bias, fairness and the way forward
- MAI631 Unit 7 – eXplainable AI (XAI)
- MAI631 Unit 8 – AI & Tort Law
- MAI631 Unit 9 – Human Control
- Olympians Exercise:
MAI 642 - Deep Learning
- MAI642 Unit 1 – Introduction
- MAI642 Unit 2 – Fundamentals
- MAI642 Unit 3 – Mathematics and Learning
- MAI642 Unit 4 – Principles of Deep Neural Networks
- MAI642 Unit 5 – Convolutional Neural Networks
- MAI642 Unit 6 – Transfer Learning and Residual Networks
- MAI642 Unit 7 – Optimizing Deep Neural Networks
- MAI642 Unit 8 – Recurrent Neural Networks – Intro to Transformers
- MAI642 Unit 9 – Transformers and Attention
- MAI642 Unit 10 – Generative Adversarial Networks
- MAI642 Unit 11 – Deep Reinforcement Learning
- MAI642 Unit 12 – Emerging Topics in DL
MAI 643 - Artificial Intelligence in Medicine
- MAI643 Unit 1 – Introducing AIM and tracing its history
- MAI643 Unit 2 – AIM from Knowledge intensive to Data intensive Applications
- MAI643 Unit 3 – Probabilistic graphical models in medicine
- MAI643 Unit 4 – Clinical Data Their Acquisition, Storage and Use
- MAI643 Unit 5 – Public Health Applications and Ethics
- MAI643 Unit 6 – Medical Image Informatics
- MAI643 Unit 7 – Translational Bioinformatics
- MAI643 Unit 8 – Clinical Cognition and AI
- MAI643 Unit 9 – Time Representation and Temporal Reasoning in Medicine
- MAI643 Unit 11 – Automated Support to Clinical Guidelines and Care Plans
- MAI643 Unit 12 – Explainability in Medical AI
- MAI644 Unit 1 – Introduction to Computer Vision
- MAI644 Unit 2 – Fundamentals – Color
- MAI644 Unit 3 – Fundamentals Cameras
- MAI644 Unit 4 – Interpolation – Resizing
- MAI644 Unit 5 – Filters – Convolution
- MAI644 Unit 6 – Edges
- MAI644 Unit 7 – Features – Corners
- MAI644 Unit 8 – Feature Descriptors and Image Transforms
- MAI644 Unit 9 – RANSAC, Panorama Stitching
- MAI644 Unit 10 – Visual Recognition Segmentation
- MAI644 Unit 11 – Visual Recognition – Image Classification
- MAI644 Unit 12 – Visual Bag of Words
- MAI644 Unit 13 – Object Detection
- MAI644 Unit 14 – Camera Models
- MAI644 Unit 15 – Camera Calibration
- MAI644 Unit 16 – Stereo Vision
- MAI645 Unit 1 – Introduction
- MAI645 Unit 2 – Image Classification
- MAI645 Unit 3 – Image Classification: Regularization, Optimization, Backpropagation
- MAI645 Unit 4 – Image Classification: CNN Architectures
- MAI645 Unit 5 – Training and Visualization
- MAI645 Unit 6 – Object Detection
- MAI645 Unit 7 – Recurrent Neural Networks
- MAI645 Unit 8 – Video Understanding, Generative Models, & Self-Supervised Learning
- MAI645 Unit 9 – 3D Vision
- MAI645 Unit 10 – Character Animation
- MAI646 Unit 1 – Cognitive Systems
- MAI646 Unit 2 – Argumentation in AI: Motivation
- MAI646 Unit 3 – Argumentation in AI: Theory
- MAI646 Unit 4 – Evaluation of Arguments
- MAI646 Unit 5 – Structured Argumentation
- MAI646 Unit 6.1 – Gorgias Framework
- MAI646 Unit 6.2 – Gorgias Technicalities
- MAI646 Unit 7 – Cognitive Assistants via Argumentation
- MAI646 Unit 8 – Methodology for Cognitive Decision Making
- MAI646 Unit 9 – Argumentation for Human-Centric Applications
- MAI646 Unit 10 – From SBPS to Argumentation in Gorgias
- MAI646 Unit 11 – Argumentation and Ethical AI Systems
- MAI646 Unit 12 – Summary and Recap of Course
- MAI646 – Research Study Assignments
- MAI648 Unit 1 – Introduction
- MAI648 Unit 2 – Human-Computer Interaction Principles
- MAI648 Unit 3 – Human-Centered Design
- MAI648 Unit 4 – Human Cognitive Factors in Intelligent Interactive Systems
- MAI648 Unit 5 – Affective Computing
- MAI648 Unit 6 – Adaptive User Interfaces
- MAI648 Unit 7 – Midterm Revision and Student Presentations
- MAI648 Unit 8 – Adaptive Usable Security
- MAI648 Unit 9 – Intelligent Biometrics
- MAI648 Unit 10 – Conversational UIs
- MAI648 Unit 11 – Explainable AI
- MAI648 Unit 12 – Evaluating Intelligent User Interfaces
- MAI648 Unit 13 – Final Revision and Group Project Presentations
- Labs:
- Lab 1 – Introduction to the Laboratories-Tutorials
- Lab 2 – Eye Tracking Research and its Applications in IUI
- Lab 3 – Intelligent User Interface Design Prototyping – Part 1
- Lab 4 – Intelligent User Interface Design Prototyping – Part 2
- Lab 5 – Python Programming
- Lab 6 – Emotion Recognition
- Lab 7 – Modeling Human Factors with Machine Learning
- Lab 8 – Face-based User Identification
- Lab 9 – Voice-based User Identification
- Lab 10 – Designing and Developing Skills in Alexa
- Assignments and Project:
- Assignment 1 – Everyday Devices with an Intelligent User Interface
- Assignment 2 – Personas and Usage Scenarios
- Assignment 3 – Analysis, Report and Presentation of a Research Paper in Intelligent User Interfaces
- Group Project – Analysis, Design and Development of a Prototype Intelligent User Interface
MAI 650 - Internet of Things
- MAI650 Unit 1 – Introduction
- MAI650 Unit 2 – IoT Devices – Introduction
- MAI650 Unit 2 – IoT Devices – Core
- MAI650 Unit 2 – IoT Devices – Advanced
- MAI650 Unit 3 – Communication Technology – Introduction
- MAI650 Unit 3 – Communication Technology – Core
- MAI650 Unit 3 – Communication Technology – Advanced
- MAI650 Unit 4 – Architectural Design and Applications in IoT – Introduction
- MAI650 Unit 4 – Architectural Design and Applications in IoT – Core
- MAI650 Unit 4 – Architectural Design and Applications in IoT – Advanced
- MAI650 Unit 5 – Security and Privacy in IoT – Introduction
- MAI650 Unit 5 – Security and Privacy in IoT – Core
- MAI650 Unit 5 – Security and Privacy in IoT – Advanced
- MAI650 Unit 6 – Business Value – Introduction
- MAI650 Unit 6 – Business Value – Core
- MAI650 Unit 6 – Business Value – Advanced