Certificate Course in Artificial Intelligence for Professionals
Building your own trustworthy AI solutions
Overview
The Certificate Course in Artificial Intelligence for Professionals is a short-term training programme offered by the Department of Computational Mathematics, Faculty of Information Technology, University of Moratuwa, in response to the growing demand for AI expertise across industries.
The course is designed for professionals from diverse domains who wish to gain practical knowledge and hands-on experience in artificial intelligence, intelligent automation, predictive analytics, generative AI and trustworthy AI systems.
Participants learn how to conceptualize, design and implement AI-based solutions for real-world applications, enabling data-driven innovation and informed decision making within their own professional environments.
COURSE AT A GLANCE
Programme Objectives
Build Practical Competency
Gain a comprehensive understanding of AI and the practical skills to design, develop and deploy trustworthy AI solutions within your own domain.
Bridge Theory and Practice
Critically appraise a broad range of data-driven and knowledge-driven AI techniques, including machine learning, generative AI, LLM transformers, RAG and agentic AI.
Champion Responsible AI
Develop a critical awareness of the ethical, legal, accountability, fairness and social considerations involved in deploying AI solutions at scale.
Who Should Attend
Working Professionals
Professionals from any industry or domain who want to apply AI within their own work.
Managers and Decision Makers
Leaders exploring how AI can be adopted within their teams and organizations.
Engineers and Analysts
Technical staff seeking hands-on experience in building and evaluating AI solutions.
Responsible AI Practitioners
Anyone building AI systems that need to be trustworthy, fair and accountable.
Programme Outline
Module 1: Comprehensive Introduction to AI
- •Theoretical foundations of AI
- •Evolution of paradigms for constructing AI solutions
- •Creative synergy across paradigms and the road ahead
- •Future challenges and the question of singularity
- •Hallucination and trustworthiness of AI systems
- •Principles of responsible AI
Module 2: Major Paradigms for Building AI Solutions
- •Knowledge-driven and data-driven approaches
- •Knowledge engineering, representation and knowledge graphs
- •Large language models in context
- •The emergence of large datasets and data-driven machine learning
- •Issues with each paradigm and how they complement one another
Module 3: Machine Learning and Deep Learning
- •Regression and classification techniques
- •Clustering methods
- •Deep learning architectures, including CNNs and RNNs (conceptual)
- •Hands-on application development
Module 4: LLMs and Generative AI
- •Natural language processing, LLMs and the transformer architecture
- •Generative AI, hallucination and explainability
- •Trustworthiness, retrieval-augmented generation (RAG) and agentic AI
- •Hands-on application development
Key Dates
Application deadline
Course commencement
Certificate awarded by the University of Moratuwa
Delivery & Assessment
Session Time
Saturdays, from 8.00 a.m. to 12 noon.
Mode of Delivery
All sessions are delivered live online, combining conceptual grounding with hands-on application development.
Assessment
- •Continuous assessment conducted online
- •Online quizzes and MCQ examinations
- •Assignments
- •Mini projects
Certification
Participants who successfully complete the training programme are awarded a certificate from the University of Moratuwa.
Course Fees
Bank Payment Details
How to Apply
Complete the online application form with your details.
Pay the application fee and course fee through Bank of Ceylon or online banking, quoting AI for Professionals as the reason for payment.
Receive confirmation from the coordinating team before the course begins.
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Programme Contact
Address
Department of Computational Mathematics
Faculty of Information Technology
University of Moratuwa
Moratuwa, Sri Lanka 10400
Programme Coordinator
Prof. (Mrs.) A.T.P. Silva
Professor in Artificial Intelligence, Department of Computational Mathematics