Executive Diploma in Advanced AI Systems and Applications

Course Dates :

06/10/25

25

Course ID:

251006001002447LUEMT

Course Duration :

25 Studying Day/s

Course Location:

Course Category:

Executive Diploma

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 Diploma Certificate

Leading to:
Executive Mini Masters Certificate
Leading to
Executive Masters Certificate

Certification Will Be Issued From : From London, United Kingdom

Course Information

Introduction

The Executive Diploma in Advanced AI Systems and Applications is designed to provide senior professionals with the advanced knowledge and practical skills necessary to excel in the rapidly evolving field of Artificial Intelligence (AI). The course covers a comprehensive curriculum that focuses on the key areas of AI, including Computer Vision, Robotics and AI, Reinforcement Learning, and Data Science and AI Foundations. This diploma program equips participants with the technical expertise and strategic understanding required to lead and manage AI-driven projects and initiatives across industries.

The course is structured over 20 days of intensive learning, combining theoretical knowledge with hands-on practical applications. Each module is crafted to give participants a deep understanding of the core concepts of AI systems and their real-world applications. To further enhance learning, participants are required to prepare a final thesis of 4,500 - 5,000 words within an additional 5 days. The final thesis is an opportunity for participants to demonstrate their ability to integrate and apply the knowledge gained throughout the course to a real-world AI project or research topic.

This advanced diploma program is meticulously designed to prepare professionals for high-level roles in AI, offering an in-depth exploration of the technologies and methodologies that drive innovation in this field. The four core modules—Computer Vision, Robotics and AI, Reinforcement Learning, and Data Science and AI Foundations—will equip participants with the necessary tools and frameworks to tackle complex AI challenges and contribute to the development of cutting-edge AI applications.

Throughout the 20-day learning period, participants will engage in a combination of lectures, case studies, group discussions, and hands-on exercises. This approach ensures that participants not only understand the theoretical underpinnings of AI but also gain practical insights into how these technologies are applied in diverse industries. The final thesis will serve as a capstone project, allowing participants to apply their learning to a specific AI application, research problem, or industry challenge.

The course is structured to maximize learning and application, ensuring that participants not only grasp the concepts but also gain the ability to lead AI projects, innovate in AI-driven industries, and contribute to the evolving landscape of AI systems and applications.

Upon completion of the Executive Diploma in Advanced AI Systems and Applications, participants will have a robust understanding of AI technologies and their practical implementation. The course’s detailed curriculum, including the specialized modules in Computer Vision, Robotics and AI, Reinforcement Learning, and Data Science and AI Foundations, will enable professionals to make informed decisions and lead AI-driven initiatives. Participants will also gain the skills necessary to research, evaluate, and apply AI systems in diverse organizational contexts, positioning them as leaders in the AI domain.

The program’s balanced approach, combining theoretical foundations with practical application, ensures that participants are fully equipped to drive AI innovation. The final thesis offers an opportunity for deeper exploration and application of AI concepts, culminating in a comprehensive research paper that reflects the knowledge and skills acquired throughout the course.

Objectives

The Executive Diploma in Advanced AI Systems and Applications aims to achieve the following objectives:

To provide participants with advanced knowledge in AI systems, focusing on key areas such as Computer Vision, Robotics, Reinforcement Learning, and Data Science.
To equip professionals with the practical skills needed to design, implement, and manage AI systems and applications across various industries.
To enable participants to lead AI-driven projects and initiatives, making strategic decisions based on AI technologies.
To foster a deep understanding of AI's theoretical foundations and real-world applications.
To provide participants with the tools and techniques necessary to conduct research in AI and to develop innovative AI solutions.
To prepare professionals for leadership roles in AI-related fields, enhancing their ability to innovate and manage complex AI systems and applications.

Who Should Attend?

This Executive Diploma in Advanced AI Systems and Applications is specifically designed for:

Senior professionals, managers, and executives who are looking to enhance their understanding of AI and its application in various industries.
Technology leaders, engineers, and researchers who wish to gain advanced knowledge of AI systems and applications to lead AI-driven projects.
Professionals in industries such as healthcare, finance, manufacturing, retail, and robotics, who want to leverage AI to drive innovation within their organizations.
Individuals with a background in technology or engineering who are seeking to transition into AI-focused roles or enhance their expertise in AI.
Entrepreneurs and business leaders who aim to integrate AI technologies into their business models and stay ahead in the rapidly advancing AI landscape.

This program is ideal for individuals who are passionate about AI, eager to develop leadership capabilities, and committed to advancing their careers in AI systems and applications.

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 / 5

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 3 / 5

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 4 / 5

Data Science and AI Foundations

Day 1:
Foundations of Data Science

Introduction to data science and its interdisciplinary applications.
Overview of the data lifecycle: Collection, cleaning, storage, and retrieval.
Tools and technologies: Excel, SQL, and Python basics.
Case study: Transforming raw data into structured formats for analysis.


Day 2:
Exploratory Data Analysis (EDA)

Statistical measures: Mean, median, mode, variance, and correlation.
Data visualization techniques using Matplotlib and Seaborn.
Identifying outliers and handling missing data.
Hands-on exercise: Conducting EDA on a sample dataset.


Day 3:
Machine Learning Fundamentals

Supervised vs. unsupervised learning: Definitions and examples.
Regression and classification algorithms: Linear regression, logistic regression.
Clustering methods: K-means and hierarchical clustering.
Practical session: Building a simple predictive model.


Day 4:
Advanced AI Concepts and Applications

Neural networks and deep learning fundamentals.
Introduction to natural language processing (NLP).
Explainable AI (XAI): Techniques for model transparency.
Group activity: Designing an AI solution for a hypothetical scenario.


Day 5:
Ethics, Compliance, and Implementation

Ethical considerations in AI development and deployment.
Regulatory frameworks governing data privacy and security.
Strategies for integrating AI into organizational workflows.
Final project presentation and feedback session.

Executive Diploma Thesis

Final Paper: 4,500 - 5,000 Words