Executive Master in Advanced AI Technologies and Systems

Course Dates :

10/03/25

09/05/25

Course ID:

250310001004583LUI

Course Fees GBP:

£39,738.45

Course Category:

Executive Masters

Leadership, Artificial intelligence

Course Certified By:

* LondonUni
For Executive Management Training

* Executive Masters Certificate

Certification Will Be Issued From :

United Kingdom

Please Note : Your £250.00 Deposit will be deducted from the total invoice Amount.
To commence the registration process for your training course, please follow the link provided and proceed with; Upon successful payment, we will promptly contact you to finalize your enrollment and issue a confirmation of your guaranteed placement.

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

The 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

Please Note : Your £250.00 Deposit will be deducted from the total invoice Amount.
To commence the registration process for your training course, please follow the link provided and proceed with; Upon successful payment, we will promptly contact you to finalize your enrollment and issue a confirmation of your guaranteed placement.

Part 2 / 9

Neural Networks and Deep Learning

Day 1: Introduction to Neural Networks and Deep Learning

Overview of Neural Networks and their Importance in AI.
Understanding the Biological Inspiration behind Neural Networks.
Fundamentals of Perceptrons and Multilayer Perceptrons (MLPs).
Key Concepts: Activation Functions, Weights, and Bias.

Day 2: Training Neural Networks

Forward Propagation and Backpropagation Algorithms.
Loss Functions and Optimization Techniques (e.g., Gradient Descent).
Avoiding Overfitting: Regularization Techniques and Dropout.
Evaluating Model Performance with Metrics.

Day 3: Deep Learning and Advanced Architectures

Introduction to Deep Neural Networks (DNNs).
Convolutional Neural Networks (CNNs) for Image Processing.
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks.
Autoencoders and their Applications in Dimensionality Reduction.

Day 4: Tools and Frameworks for Neural Networks

Overview of Popular Deep Learning Libraries: TensorFlow, Keras, and PyTorch.
Building and Training Neural Networks with TensorFlow and Keras.
Practical Implementation: Case Study on Image Recognition.
Debugging and Optimizing Deep Learning Models.

Day 5: Applications and Future Trends

Neural Networks in Natural Language Processing (NLP).
Leveraging Neural Networks for Predictive Analytics.
Ethical Considerations and Challenges in Deep Learning.
Future Trends in Neural Networks and AI.

Part 3 / 9

Natural Language Processing (NLP)

Part 4 / 9

Reinforcement Learning

Part 5 / 9

Computer Vision

Day 1: Introduction to Computer Vision and Image Processing

Overview of computer vision: History, applications, and future trends
Fundamental concepts in image processing: Pixels, resolution, and color models
Image pre-processing techniques: Filtering, resizing, and normalization
Feature extraction methods: Edge detection, histograms, and keypoints

Day 2: Machine Learning Foundations for Computer Vision

Introduction to machine learning: Supervised and unsupervised learning techniques
Training models for image classification: Logistic regression and support vector machines
Feature selection and dimensionality reduction methods
Evaluating model performance: Accuracy, precision, recall, and F1 score

Day 3: Deep Learning for Computer Vision

Introduction to deep learning and neural networks
Understanding Convolutional Neural Networks (CNNs) and their applications in computer vision
Implementing CNN models using TensorFlow and PyTorch
Transfer learning: Leveraging pre-trained models for better performance

Day 4: Advanced Computer Vision Techniques

Object detection methods: YOLO, R-CNN, and SSD
Image segmentation techniques: Semantic segmentation and mask R-CNN
Facial recognition and emotion detection: Leveraging computer vision for biometric applications
Real-time video processing and tracking

Day 5: Applications and Future Trends in Computer Vision

Industry-specific applications of computer vision: Healthcare, automotive, and retail
Ethics and challenges in implementing computer vision systems
Future trends in computer vision: AI-powered vision systems and edge computing
Capstone project: Hands-on implementation of a computer vision system

Part 6 / 9

Robotics and AI

Part 7 / 9

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

25000 - 30000 Words

Please Note : Your £250.00 Deposit will be deducted from the total invoice Amount.
To commence the registration process for your training course, please follow the link provided and proceed with; Upon successful payment, we will promptly contact you to finalize your enrollment and issue a confirmation of your guaranteed placement.