HCAIM Content

Modelling (Module A)

Technical focus: Foundation of AI

General AI

Lecture: Historical Introduction to Scientific Explanation Models

Lecture: Understanding Data

Data Exploration for Machine Learning

Tutorial: Understanding Data

Lecture: Exploratory Data Analysis

Tutorial: Exploratory Data Analysis

Lecture: Inference and Prediction

Tutorial: Inference and Prediction

Machine Learning Fundamentals

Lecture: Model Evaluation

Tutorial: Model Evaluation

Lecture: Model Fitting and Optimization

Practical: Model Fitting and Optimization

Decision Theory

Lecture: Decision Theory

Tutorial: Decision Theory

Lecture: Decision Networks

Tutorial: Decision Networks

Practical focus: AI modelling

Data Science

Lecture: The Data Analysis Process

Practical: Platforms

Lecture: Data Preparation and Exploration

Practical: Data Preparation and Exploration

Supervised Machine Learning

Lecture: Linear Regression

Practical: Linear Regression

Lecture: Decision Trees

Practical: Decision Trees

Lecture: SVMS and Kernels

Practical: SVMS and Kernels

Lecture: Neural Networks

Unsupervised Machine Learning

Lecture: Unsupervised Learning

Practical: Unsupervised Learning

ML applications

Lecture: Natural Language Processing

Practical: Natural Language Processing

Ethical focus: Ethics fundamentals

General Ethics

Lecture: Introduction to Human-Centered AI

Ethical Frameworks

Interactive session: Ethical Frameworks

Lecture: Utilitarianism

Interactive session: Utilitarianism

Lecture: Virtue Ethics

Interactive session: Virtue Ethics

Lecture: Duty Ethics

Interactive session: Duty Ethics

Lecture: Theory of Justice

Advanced Ethics

Lecture: Social Contract Theories

Applied Ethics

Lecture: Value-Sensitive Design

Interactive session: Value-sensitive Design

Lecture: Privacy

Lecture: Ethics of Decision Support Systems

Lecture: Decision making and (cognitive) biases

Deployment (Module B)

Technical focus: deep learning

Fundamentals of Deep Learning

Tutorial: Fundamentals of deep learning

Practical: Fundamentals of deep learning

Optimization of Deep Learning

Lecture: Regularization

Tutorial: Regularization

Lecture: Batch processing

Tutorial: Batch processing

Applications of Deep Learning

Lecture: Building computational graphs, modern architectures

Lecture: Convolutional Neural Networks

Tutorial: Convolutional Neural Networks

Practical: Convolutional Neural Networks

Lecture: Recurrent Neural Networks

Lecture: Transformer networks

Tutorial: CNNs and Transformers for images

Lecture: Hardware and software frameworks for deep learning

Deriving and Implementing Backpropagation

Lecture: Derivation and application of backpropagation

Tutorial: Derivation and application of backpropagation

Forward pass

Lecture: Forward propagation

Tutorial: Forward propagation

Hyperparameter tuning

Lecture: Hyperparameter tuning

Tutorial: Hyperparameter tuning

Practical focus: Organisational AI

MLOps

Lecture: ML-Ops

Tutorial: ML-Ops

Lecture: ML-Ops Lifecycle

Practical: ML-Ops Lifecycle

Deployment of AI

Tutorial: Data architecture

Interactive session: Data architecture

Practical: Hadoop-based technologies

Quality of Development & Deployment

Lecture: CI/CD

Ethical focus: Trustworthy AI

General Explainable AI

Lecture: Introduction General Explainable AI

Practical: Practice with XAI models 1

Practical: Practice with XAI models 2

Privacy

Lecture: Introduction to privacy and risk

Interactive session: Perspectives on privacy

Practical: Auditing frameworks of privacy and data protection

Lecture: Privacy and machine learning

Practical: Applying and evaluating privacy-preserving techniques

Security and robustness

Lecture: Security and robustness

Practical: Apply auditing frameworks

Practical: Enhancing ML security and robustness

Risk

Lecture: Risk & Risk mitigation

Interactive session: Risk & Risk mitigation

Practical: Risk & Risk mitigation

Evaluation (Module C)

Technical focus: Future AI

Introduction

Lecture: Introduction to the resurgence of AI and ML

Lecture: Guest Lecture on Future of AI

Open Problems and Challenges

Lecture: Guest Lecture on Explainable Machine Learning (XAI)

Practical: Explainable Machine Learning (XAI)

Lecture: Trust, Normativity and Model Drift

Interactive Session: Trust, Normativity and Model Drift

Interactive Session: Privacy Preserving Machine Learning

Lecture: Generalizability and Artificial General Intelligence (AGI)

Advances in ML Models Through an HC Lens. A Result-Oriented Study

Lecture: Semi-supervised and Unsupervised Learning

Lecture: Generative Models, Transform Deep Learning and Hybrid learning models

Lecture: Theory of Federated Learning (Profiling and Personalization)

Lecture: Federated Learning – Advances and Open Challenges

Practical: Federated Learning – Train deep models

Lecture: Model Compression – Edge Computing

Practical: Model Compression – Edge Computing

Emerging Evaluations for HCAI Models – Discussion-Based Study

Lecture: Trust Models and Trust quantification

Philosophical Discussion on Future AI technology

Interactive Session: Living with Robots

Practical focus: Socially responsible AI

Scope Of Socially Responsible AI

Lecture: Positive And Negative Externalities

Corporate Social Responsibility (ISO 26000) – When Using HCAI System

Lecture: Fair Operating Practices – AI Recruitment And Malpractices Of AI Monitoring

Interactive Session: AI-Based Decision Making – Recruitment And Promotion

Interactive Session: Decision Making Based On AI Monitoring

Interactive Session: Human Intervention On Inconsistent And/Or Good AI Decisions

Interactive Session: Transfer Of Control Back And Forth Between Human And AI

Interactive session: Psychological Aspects when working with AI - stress, anxiety, depression

Lecture: Consumer Issues – Filter Bubbles, Data Storage, AI Monitoring, Fair Practices

Interactive Session: Consumer Issues – Filter Bubbles, Data Storage, AI Monitoring, Fair Practices

Interactive Session: – Community Development – Societal Impact Assessment Prior To Working On AI Project

Socio-Legal Aspects For AI

Interactive Session: Who Is Responsible? – Product Responsibility, Copyright Problems

AI For All

Lecture: Economic Gaps – Digital Divide

Interactive Session: Economic Gaps – Digital Divide In Categories: Geographical, Technical, Financial And Political

interactive-session-how-ai-affects-human-behaviour-eg-mobility-positive-and-negative

Interactive Session: Environment Impact – Carbon Footprint

Interactive Session: Education Impact – Auto AI Decision Making

Interactive Session: Filter Bubble – Political, Corporate And Geographical

Interactive Session: AI-Powered Warfare And International Peace

Ethical focus: Compliance, Legality, Humanity

EU And International Legislation/Frameworks On Data, AI, Human Rights And Equality

Lecture: Overview Of Ethical, Professional And Legal Aspects Of HCAI Applications

Interactive Session: Ethical, Professional And Legal Aspects Of HCAI Applications

Lecture: Data And Its Challenges – EU GDPR, US COPPA, HIPPA

Lecture: Data and its challenges - Data Regulations, Data Sourcing and HCAI prospective

Interactive Session: Data And Its Challenges. How GDPR Impacts AI Solutions

Lecture: EU Human Rights Legislation

Interactive Session: EU Human Right Legislation – A Case Study

Lecture: EU Proposal Of Regulation On HCAI Applications

Interactive Session: EU Proposal Of Regulation On AI – A Case Study

Practical: Effective Of EU Proposal Of Regulation On AI

Lecture: Strengths And Limitations Of Existing Laws A Deeper Dive

Data Management, Audit And Assessment

Lecture: Data Security And Compliance, Data Lineage And Management

Lecture: Governance And Stewardship, Key Stakeholders And Personal Data Management

Practical: Common Roles And Cross Overs Between Data Management And AI Teams

Practical: Investigate Data Lineage, Challenges And Potential Impact Of The AI Teams

Graduation (Module D)

Ethical focus: Ethics fundamentals

Ethical focus: Research in Practice

Interactive session: Ethics in research

Interactive: Qualitative methods in data gathering

Lecture: Conducting a literature review

Lecture: Critically reviewing sources

Lecture: Research design

Lecture: Research proposal writing

Tutorial: Research portfolio

Tutorial: Writing a scientific research paper/work

Tutorial: Project management

Practical: Presenting statistical data