Course Structure

The Advanced Diploma in Artificial Intelligence is structured into six comprehensive modules, each focusing on a key area of AI. The course is designed to be completed in six months, with classes held two days a week. Each module is designed to build upon the knowledge gained in previous modules, providing a progressive learning experience.

Module 1: INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Overview of AI : Understanding the history, development, and scope of artificial intelligence.
• AI Disciplines : Overview of various AI disciplines including machine learning, neural networks, natural language processing, and robotics.
• The Role of AI in the UK : Examination of how AI supports businesses, government, and individuals in the UK.

Module 2: MACHINE LEARNING

• Fundamentals of Machine Learning : Principles and practices of machine learning algorithms.
• Supervised and Unsupervised Learning : Techniques for training models with and without labelled data.
• Evaluation and Validation : Methods for evaluating and validating machine learning models.
• Practical Applications : Implementation of machine learning algorithms in real-world scenarios.

Module 3: NEURAL NETWORKS AND DEEP LEARNING

• Introduction to Neural Networks : Understanding the basics of neural networks and their architectures.
• Deep Learning Techniques : Advanced methods for training deep neural networks.
• Convolutional Neural Networks (CNNs) : Techniques for image and video processing.
• Recurrent Neural Networks (RNNs) : Methods for sequential data processing and time series analysis.

Module 4: NDAENTTUARLA PL RLAONSTGHUEATGICES P &RO OCRETSHSOINDGO (NNTLICP)S

• Fundamentals of NLP : Techniques for processing and analysing human language.
• Text Analysis and Sentiment Analysis : Methods for extracting meaning and sentiment from text data.
• Speech Recognition and Generation : Techniques for converting speech to text and vice versa.
• Chatbots and Virtual Assistants : Development and implementation of conversational AI systems.

Module 5: ROBOTICS AND AUTONOMOUS SYSTEMS

• Introduction to Robotics : Principles of robot design and control.
• Autonomous Navigation : Techniques for enabling robots to navigate and interact with their environment.
• Sensor Integration : Methods for integrating sensors and actuators in robotic systems.
• Robotic Process Automation (RPA) : Implementation of automation solutions using robotic systems.

Module 6: AI ETHICS AND GOVERNANCE

• Ethical Considerations in AI : Examination of ethical issues in AI development and deployment.
• AI Regulations in the UK : Overview of key regulations and standards governing AI in the UK.
• AI Governance : Techniques for ensuring transparency, accountability, and fairness in AI systems.
• Case Studies and Practical Exercises : Analysis of real-world AI applications and ethical dilemmas

Course Delivery

The course will be delivered through a combination of in-person and online methods, ensuring flexibility & accessibility for all students. Classes will be held two days a week.

• Lectures : Delivered by experienced AI professionals and researchers, available both in-person and online.
• Laboratory Sessions : Hands-on training in state-of-the-art AI labs, providing practical experience with AI techniques.
• Workshops : Interactive workshops focusing on specific AI techniques and procedures.
• Online Resources : Access to a comprehensive online library, learning management system, and digital resources.
• Guest Lectures : Insights from professionals working in various AI fields, sharing their experiences and expertise.
• Field Visits : Organised visits to AI research centres, businesses, and other relevant facilities.
• Placements : Opportunities for work placements in AI firms or IT departments, providing practical experience & industry exposure.

Eligibility Criteria

To be eligible for the Advanced Diploma in Artificial Intelligence, applicants must meet the following criteria:

• Educational Background : A bachelor's degree in a related field such as Computer Science, Information Technology, Mathematics, Statistics, or a closely related discipline.
• Academic Performance : A minimum 2:1 honours degree or equivalent.
• Language Proficiency : Proficiency in English is required. Non-native English speakers must provide IELTS scores of at least 6.5 overall, with no lower than 6.0 in any component.
• Technical Skills : Basic knowledge and experience in programming, algorithms, and data structures are recommended.
• Background Check : Applicants may be required to undergo a background check due to the sensitive nature of work in AI development.

Sample Certificate