Undergraduate Certification in Generative AI Engineering

Core Periods Modules

Programme Structure The Undergraduate Certification in Generative AI Engineering is a comprehensive 18-month journey, strategically divided into three interconnected 6-month phases designed for a holistic and practical learning experience. Phase 1: Immersive Learning (Months 1-6) focuses on building a robust theoretical and foundational understanding. This includes core curriculum in mathematics for AI, machine learning fundamentals, deep learning architectures, and the principles of various generative models (GANs, VAEs, Transformers, Diffusion Models). Participants engage in hands-on projects, implementing algorithms and building models from scratch, while gaining proficiency in Python with AI frameworks like TensorFlow and PyTorch. Expert-led sessions and workshops cover ethical considerations, prompt engineering, and emerging research. Phase 2: Intensive Internship (Months 7-12) transitions participants into practical application. This involves industry placements within leading AI companies or research labs, applying learned concepts to real-world challenges under the guidance of industry professionals. Interns collaborate on ongoing projects, directly applying technical skills in data preprocessing, model training, evaluation, and deployment, while building valuable industry connections. Phase 3: Industry Experience Module (Months 13-18) solidifies expertise and prepares graduates for impactful careers. This phase involves advanced, capstone-level projects simulating real industry scenarios, with a strong focus on deploying generative AI models into production and understanding MLOps best practices. Optional specialization tracks are available for focused study, alongside professional development workshops on career strategies, AI ethics in practice, and portfolio building, ensuring graduates are industry-ready leaders.

1. Period 0

   AI & Deep Learning Foundations

   Module 1.1: Introduction to AI & ML: Overview of AI, machine learning paradigms (supervised, unsupervised), and key applications. Module 1.2: Python for Data Science: Advanced Python programming, essential libraries (NumPy, Pandas, Matplotlib), data manipulation, and basic statistics. Module 1.3: Mathematical Foundations: Introduction to linear algebra, calculus, and probability concepts crucial for AI.

2. Period 1

   Core Deep Learning Architectures

   Module 2.1: Neural Networks: Perceptrons, multi-layer perceptrons, activation functions, backpropagation. Module 2.2: Convolutional Neural Networks (CNNs): Architectures, image classification, and introduction to computer vision tasks. Module 2.3: Recurrent Neural Networks (RNNs) & LSTMs: Processing sequential data, basic natural language processing (NLP) concepts.

3. Period 2

   Introduction to Generative AI & Key Models

   Module 3.1: VAEs for Data Generation: Theoretical understanding and practical implementation of VAEs for latent space exploration and data synthesis. Module 3.2: GANs: Theory and Practice: Core components of GANs, common architectures (DCGAN), and hands-on image generation projects. Module 3.3: Deep Learning Frameworks in Practice: Building and training VAEs and GANs using TensorFlow and PyTorch.

4. Period 3

   Transformer Models & Prompt Engineering

   Module 4.1: Transformer Architecture: Self-attention, encoder-decoder models, and their role in modern AI. Module 4.2: Large Language Models (LLMs) & NLP: Introduction to pre-trained LLMs (e.g., GPT, BERT), tokenization, and basic NLP tasks like text generation and summarization. Module 4.3: Prompt Engineering Fundamentals: Techniques for effective prompting, controlling LLM outputs, and understanding prompt biases.

5. Period 4

   Modern Generative Models & Multimodality

   Module 5.1: Diffusion Models: Understanding the principles and applications of diffusion models for high-fidelity image and content generation. Module 5.2: Multimodal Generative AI: Concepts of combining different data types (text-to-image, image-to-audio) and exploring frameworks like Stable Diffusion. Module 5.3: Ethical AI & Responsible Development: Discussing biases, fairness, privacy, and the societal impact of generative AI.

6. Period 5

   Generative AI Capstone Project

   Module 6.1: Project Definition & Planning: Selecting a project, defining scope, and outlining methodology for a significant generative AI application. Module 6.2: Model Development & Iteration: Implementing, training, and fine-tuning a generative AI model for the chosen project. Module 6.3: Evaluation & Presentation: Assessing model performance, analyzing results, and presenting the final project and its implications. This module culminates in a final project defense, showcasing practical skills and a foundational portfolio.