Executive Master in Advanced AI Technologies and Systems

Course Dates :

10/03/25

45

Course ID:

250310001004583LUEMT

Course Duration :

45 Studying Day/s

Course Location:

Course Category:

Executive Masters

Subcategories: Construction Safety, Health and Wellbeing, Environmental Sustainability, Risk Management, Technical Skills Development, Leadership and Communication, Quality Assurance

Course Certified By:

* LondonUni - Executive Management Training

* Executive Masters Certificate

Certification Will Be Issued From : From London, United Kingdom

Course Information

Introduction

The Executive Master in Advanced AI Technologies and Systems is a rigorous and comprehensive program designed to equip professionals with cutting-edge knowledge and skills in artificial intelligence. This program integrates theoretical foundations with hands-on applications, offering participants a unique opportunity to master the most advanced AI technologies shaping industries globally. Participants will engage in critical analysis, innovative thinking, and ethical decision-making, preparing them to lead in the fast-evolving world of AI.

Artificial Intelligence is transforming every aspect of modern life, from healthcare and finance to transportation and entertainment. This course focuses on bridging the gap between AI theory and its practical deployment across various sectors. With a multidisciplinary approach, the program enables participants to explore AI applications, including neural networks, natural language processing, robotics, and AI ethics, providing a holistic understanding of the field.

The program is structured as a 40-day intensive learning journey, supplemented with a 5-day period dedicated to preparing a final thesis. Participants will produce a capstone thesis ranging between 25,000 to 30,000 words, allowing them to demonstrate mastery of the course material by addressing real-world challenges in AI and proposing innovative solutions. This practical output will serve as a testament to their expertise, enhancing their professional portfolios.

Each module in this executive program is crafted by AI experts and industry leaders, ensuring the content remains at the forefront of current advancements. By combining deep dives into specialized topics with interdisciplinary exploration, the program not only builds technical competencies but also fosters leadership skills essential for navigating the complexities of modern AI systems.

This Executive Master delivers far more than technical know-how; it also emphasizes the ethical and societal implications of AI, urging participants to consider how AI systems can be designed responsibly. With a focus on sustainability and innovation, the program prepares leaders to implement AI solutions that align with ethical guidelines and global regulations.

Graduates of this program will be equipped to assume leadership roles in AI-focused organizations, guide strategic AI adoption, and contribute to the ethical development of AI technologies worldwide.

Course Structure

The program is divided into eight core modules, each meticulously designed to cover key aspects of AI:

Data Science and AI Foundations
Introduces fundamental concepts of data science, machine learning, and AI, setting a solid groundwork for advanced topics.

Neural Networks and Deep Learning
Explores the architecture and applications of neural networks, emphasizing deep learning techniques in real-world scenarios.

Natural Language Processing (NLP)
Focuses on computational linguistics, enabling participants to develop systems for understanding and generating human language.

Reinforcement Learning
Examines algorithms that enable AI systems to learn optimal behaviors through trial and error in dynamic environments.

Computer Vision
Explores techniques for enabling machines to interpret and process visual data from the world, including image and video analysis.

Robotics and AI
Delves into the integration of AI in robotic systems, highlighting applications in automation, navigation, and human-robot interaction.

Big Data and AI
Covers the intersection of big data analytics and AI, focusing on handling large-scale data for intelligent decision-making.

AI Ethics and Policy
Examines the ethical considerations and policy frameworks necessary for responsible AI implementation.

The course concludes with a 5-day thesis preparation period, during which participants will consolidate their learning by developing an in-depth thesis addressing a practical AI challenge.

Conclusion

The Executive Master in Advanced AI Technologies and Systems provides a transformative educational experience, empowering participants to lead in AI-driven innovation. By mastering advanced AI concepts and understanding their ethical implications, graduates of this program will emerge as influential professionals who can shape the future of AI in their respective industries.


Participants with a background in technology, engineering, or business will find this program particularly beneficial, although it is open to professionals from diverse fields who are committed to mastering AI technologies.

Objectives

The objectives are:

To provide a comprehensive understanding of advanced AI technologies and their applications.
To equip participants with the technical skills necessary for designing and implementing AI systems.
To foster critical thinking and ethical awareness in AI development and deployment.
To enhance leadership capabilities for managing AI-driven projects and teams.
To prepare participants for addressing real-world challenges in AI through an extensive thesis project.

Who Should Attend?

This course aims at:

Senior professionals and executives seeking to deepen their expertise in AI.
Data scientists, machine learning engineers, and technologists aspiring to advance their careers.
Industry leaders responsible for strategic AI adoption in their organizations.
Policy makers and ethicists aiming to address the implications of AI in society.
Entrepreneurs looking to develop innovative AI-driven solutions.

Training Method

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a 7” Tablet containing a copy of the presentation, slides and handouts
• Post-assessment

Program Support

This program is supported by:
* Interactive discussions
* Role-play
* Case studies and highlight the techniques available to the participants.

Daily Agenda

Each course agenda will be as follows:
• Technical Session 08.30-10.00 am
• Coffee Break 10.00-10.15 am
• Technical Session 10.15-12.15 noon
• Coffee Break 12.15-12.45 pm
• Technical Session 12.45-02.30 pm
• Course Ends 02.30 pm

Part 2 / 9

Neural Networks and Deep Learning

Day 1:
Foundations of Neural Networks

Introduction to Artificial Intelligence and Machine Learning
Anatomy of a Neural Network: Layers, Nodes, and Weights
Understanding Activation Functions and Loss Functions
Hands-On Exercise: Building a Simple Perceptron Model


Day 2:
Deep Learning Architectures

Overview of Feedforward Neural Networks (FNNs)
Convolutional Neural Networks (CNNs): Theory and Applications
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Models
Case Study: Image Recognition Using CNNs


Day 3:
Advanced Techniques and Tools

Transfer Learning: Leveraging Pre-Trained Models
Hyperparameter Tuning and Optimization Strategies
Introduction to Generative Adversarial Networks (GANs)
Practical Session: Fine-Tuning a GAN for Image Generation


Day 4:
Evaluation and Deployment

Metrics for Assessing Model Performance
Cross-Validation and Overfitting Prevention Techniques
Deploying Deep Learning Models in Production Environments
Workshop: End-to-End Project Development


Day 5:
Ethics and Future Trends

Ethical Considerations in AI: Bias, Fairness, and Transparency
Interpretable AI and Explainable Models
Emerging Trends in Neural Networks and Deep Learning
Panel Discussion: Real-World Challenges and Opportunities

Part 3 / 9

Natural Language Processing (NLP)

Day 1: Introduction to NLP and Text Preprocessing

Overview of Natural Language Processing and its real-world applications.
Understanding text data: tokens, vocabulary, and corpora.
Text preprocessing: tokenization, stemming, lemmatization, and stopword removal.
Hands-on session: Cleaning and preparing textual datasets using Python and libraries like NLTK and SpaCy.

Day 2: Core NLP Techniques

Word embeddings: Word2Vec, GloVe, and FastText.
Feature extraction methods: TF-IDF and Bag of Words (BoW).
Text classification techniques: Naïve Bayes and Support Vector Machines (SVM).
Practical exercise: Building a spam detection model.

Day 3: Advanced NLP Techniques

Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).
Sentiment analysis using deep learning models.
Sequence-to-sequence models for language translation.
Project implementation: Building an LSTM-based sentiment analysis model.

Day 4: Transformers and Modern NLP Architectures

Understanding transformers and attention mechanisms.
Exploring BERT, GPT, and other pre-trained models.
Fine-tuning transformers for specific tasks.
Hands-on activity: Fine-tuning BERT for text classification.

Day 5: NLP Applications, Trends, and Ethics

Applications of NLP in chatbots, summarization, and language generation.
Ethical considerations in NLP: Bias, fairness, and responsible AI use.
Future trends in NLP and large language models.
Capstone project: Building an end-to-end NLP solution.

Part 4 / 9

Reinforcement Learning

Day 1:
Foundations of Reinforcement Learning

Introduction to RL: Key Concepts and Terminology
Understanding Markov Decision Processes (MDPs)
Exploration vs. Exploitation Trade-off
Hands-On Exercise: Simulating Simple RL Environments


Day 2:
Core Algorithms and Techniques

Dynamic Programming Methods: Policy Iteration and Value Iteration
Monte Carlo Methods for Estimating Value Functions
Temporal Difference Learning and Q-Learning
Practical Application: Solving Gridworld Problems


Day 3:
Advanced Topics in RL

Function Approximation for Large-Scale Problems
Deep Reinforcement Learning: Combining Neural Networks with RL
Policy Gradient Methods and Actor-Critic Models
Case Study: Autonomous Vehicle Navigation Using RL


Day 4:
Multi-Agent Systems and Real-World Applications

Introduction to Multi-Agent Reinforcement Learning (MARL)
Cooperative vs. Competitive Scenarios in MARL
Industry Applications: Robotics, Gaming, and Supply Chain Optimization
Group Project: Designing a Multi-Agent RL System


Day 5:
Ethical Considerations and Future Trends

Addressing Bias and Fairness in RL Models
Scalability Challenges in Deploying RL Solutions
Emerging Trends: Meta-Learning and Hierarchical RL
Final Presentations and Feedback Session

Part 5 / 9

Computer Vision

Day 1:
Foundations of Computer Vision

Introduction to computer vision: history, applications, and key challenges.
Basics of digital image processing: pixel manipulation, filters, and transformations.
Understanding color spaces and histogram analysis.
Hands-on lab: implementing basic image processing techniques using Python libraries.


Day 2:
Machine Learning for Vision

Overview of supervised and unsupervised learning in computer vision.
Feature engineering: SIFT, HOG, and other traditional methods.
Introduction to convolutional neural networks (CNNs): architecture and functionality.
Lab session: building a simple CNN for image classification.


Day 3:
Advanced Techniques and Tools

Object detection frameworks: YOLO, SSD, and Faster R-CNN.
Semantic segmentation and instance segmentation techniques.
Transfer learning and fine-tuning pre-trained models.
Practical exercise: deploying a pre-trained model for a custom dataset.


Day 4:
Real-World Applications and Ethics

Case studies: computer vision in healthcare, retail, and autonomous systems.
Addressing bias and fairness in AI models.
Regulatory compliance and data privacy considerations.
Group activity: designing an ethical AI solution for a given scenario.


Day 5:
Deployment and Future Trends

Edge computing and real-time computer vision applications.
Integrating computer vision with IoT and cloud services.
Emerging trends: generative adversarial networks (GANs) and augmented reality.
Final project presentation: participants showcase their end-to-end computer vision solution.

Part 6 / 9

Robotics and AI

Day 1:
Foundations of Robotics and AI

Overview of robotics: history, types, and applications.
Introduction to AI: machine learning, neural networks, and deep learning.
Key components of robotic systems: sensors, actuators, and controllers.
Case study: Autonomous drones in agriculture.


Day 2:
Designing Robotic Systems

Principles of mechanical design for robotics.
Programming basics for robotics: Python and ROS (Robot Operating System).
Integration of AI algorithms into robotic workflows.
Hands-on exercise: Building a simple robotic arm.


Day 3:
Ethical and Regulatory Considerations

Ethical challenges in AI and robotics: bias, transparency, and accountability.
Compliance with international standards (e.g., ISO 10218, GDPR).
Risk assessment and mitigation strategies for robotic systems.
Group discussion: Balancing innovation with responsibility.


Day 4:
Advanced Applications and Machine Learning

Collaborative robots (cobots): benefits and use cases.
Reinforcement learning for robotics: theory and practice.
Real-time data processing and decision-making in AI systems.
Workshop: Developing a machine learning model for object recognition.


Day 5:
Strategic Implementation and Future Trends

Frameworks for managing robotics projects: Agile and Design Thinking.
Emerging trends: swarm robotics, quantum computing, and edge AI.
Developing a roadmap for AI and robotics adoption in organizations.
Final project presentation: Applying course concepts to a real-world scenario.

Part 7 / 9

Big Data and AI

Day 1:
Introduction to Big Data and AI

Overview of Big Data: Characteristics, Types, and Sources
The Importance of Big Data in the Modern Business Landscape
Introduction to Artificial Intelligence: Key Concepts and Technologies
Relationship Between Big Data and AI: How They Work Together

Day 2:
Big Data Technologies and Tools

Data Collection and Storage Techniques for Big Data
Data Processing Frameworks: Hadoop, Spark, and Other Big Data Tools
Data Quality and Cleansing: Ensuring Accurate Insights
Introduction to Data Warehousing and Cloud-Based Solutions

Day 3:
Machine Learning and Deep Learning

Overview of Machine Learning: Types and Algorithms
Supervised vs. Unsupervised Learning: Key Differences and Applications
Introduction to Deep Learning and Neural Networks
Hands-On: Building a Simple Machine Learning Model

Day 4:
Advanced AI Techniques and Applications

Natural Language Processing (NLP) and Computer Vision
Reinforcement Learning and Its Applications
Case Studies: Successful AI Implementations in Various Industries
Hands-On: Training a Deep Learning Model for Image Recognition

Day 5:
Ethical Considerations and Practical Applications

Ethical Issues in Big Data and AI: Privacy, Bias, and Accountability
Best Practices for Data Security and Compliance with Regulations
Implementing AI Solutions in Business: Challenges and Strategies
Final Project: Solving a Real-World Problem Using Big Data and AI

Part 8 / 9

AI Ethics and Policy

Day 1: Foundations of AI Ethics

Introduction to AI Ethics: Concepts and Importance.
Ethical Frameworks for AI: Deontology, Utilitarianism, and Virtue Ethics.
Key Challenges in AI Ethics: Bias, Fairness, and Transparency.
The Role of Human-Centered Design in Ethical AI.

Day 2: Legal and Regulatory Frameworks

Overview of Global AI Regulations and Standards.
Privacy and Data Protection in AI Systems.
Intellectual Property Rights and AI Innovations.
Developing Organizational AI Policies: Key Considerations.

Day 3: AI Governance and Accountability

Principles of Responsible AI Governance.
Establishing Accountability Mechanisms for AI Use.
Stakeholder Collaboration for Ethical AI Practices.
Monitoring and Auditing AI Systems for Compliance.

Day 4: Societal Impacts and Ethical Challenges

Addressing Bias and Inequality in AI Applications.
The Role of AI in Disinformation and Privacy Breaches.
Ethical Dilemmas in Autonomous Systems and AI Decision-Making.
Building Public Trust in AI Technologies.

Day 5: Policy Development and Practical Application

Creating Ethical AI Policies: A Step-by-Step Guide.
Real-World Case Studies in AI Ethics and Policy.
Best Practices for Policy Implementation and Continuous Improvement.
Future Trends in AI Ethics and Policy Development.

Executive Masters
Thesis

Final Paper: 25000 - 30000 Words