Advanced Program in Generative AI and Machine Learning

Module Name: Data Analysis and Git Fundamentals

• Participants will perform data operations using Excel, SQL aggregations and Git-based version control.

• The module includes using libraries like NumPy, Pandas, Matplotlib, SciPy and Scikit-learn for exploratory data analysis.

Module Name: Data and Feature Engineering

• This module covers data cleaning, feature selection, and normalisation techniques.

• Participants will engage in hands-on exercises, case studies and classroom discussions to build expertise.

Module Name: Supervised Learning – Regression

• The module covers linear regression, evaluation metrics, polynomial regression and overfitting scenarios.

• Strategies to address overfitting and enhance model generalisability will also be introduced.

Module Name: Supervised Learning – Classification

• Topics include logistic regression, decision trees, random forests and support vector machines.

• Model deployment basics such as saving, loading and predicting will also be introduced.

Module Name: Unsupervised Learning

• Participants will apply distance metrics and clustering algorithms such as agglomerative clustering.

• Techniques like PCA, association rule mining, DBSCAN and anomaly detection will be explored.

Module Name: Ensemble Modelling

• The module covers bagging and boosting methods, including Adaboost, XGBoost, CatBoost and Gradient Boosting.

• The differences between supervised, unsupervised and ensemble approaches will be discussed in context.

Module Name: Time Series Modelling

• Learners will work with types of time series data, ARIMA, AR, MA, and Facebook Prophet.

• They will apply forecasting techniques to practical data scenarios.

Module Name: Recommendation Engine

• Topics include similarity measures like Pearson correlation and cosine distance, and recommendation types.

• Learners will explore various libraries and frameworks to build personalised recommendation engines.

Module Name: Model Evaluation and Deep Learning Fundamentals

• Topics include cross-validation, activation functions and popular DL frameworks.

• Learners will also practise neural network implementation for common use cases.

Module Name: Neural Network Basics

• Learners will learn about perceptrons, MLPs and the mathematics behind forward propagation.

• Python-based implementation of neural networks will be covered.

Module Name: Deep Learning Optimisation

• The module covers optimisers, activation and loss functions, batch normalisation and dropout techniques.

• Best practices in selecting suitable functions for various model types will also be addressed.

Module Name: Convolutional Neural Networks (CNNs)

• Topics include filters, pooling, padding, strides and CNN architecture.

• Learners will also work with pre-trained models, transfer learning and fine-tuning.

Module Name: Recurrent Neural Networks (RNNs)

• Learners will explore the structure and applications of RNNs, LSTMs, GRUs and attention mechanisms.

• The module also introduces transformers for sequence-based tasks.

Module Name: Unsupervised Learning – Advanced

• Topics include autoencoders, Deep Belief Networks and Restricted Boltzmann Machines.

• Learners will explore their application in real-world scenarios.

Module Name: Generative AI

• This includes Variational Autoencoders, GANs, WGANs, DCGANs and training methodologies.

• Participants will build and apply generative architectures for text and image tasks.

Module Name: Natural Language Processing (NLP)

• Topics include preprocessing, tokenisation, word embeddings, and sequence modelling.

• Learners will use models like RNNs, LSTMs and GRUs for NLP applications.

Module Name: Transformers

• Learners will understand attention mechanisms and architectures like GPT, BERT and T5.

• Use cases include machine translation, summarisation and text generation.

Module Name: Computer Vision

• The module includes image processing, object detection, segmentation and video analysis.

• Topics such as self-supervised and few-shot learning will also be explored.

Module Name: Reinforcement Learning

• Learners will explore key concepts, Bellman equations and Markov Decision Processes.

• Techniques like temporal difference learning, Monte Carlo methods and Q-learning will be implemented.

Module Name: Deep Reinforcement Learning

• Topics include DQN architectures, policy gradients, hierarchical and multi-agent RL.

• Participants will work on real-world applications of deep RL.

Module Name: Large Language Models (LLMs)

• Topics include pretraining, fine-tuning, prompt engineering and vector embeddings.

• Applications in text generation, content creation and automation will be covered.

Module Name: Agentic AI and RAG

• Topics include memory, goal setting, vector databases like FAISS, and RAG architecture.

• Participants will integrate retrieval with generation for improved contextual accuracy.

Module Name: Ethical AI

• Topics include responsible AI principles, bias mitigation, LIME, SHAP, and audit tools.

• Legal frameworks like GDPR, DPDP and the EU AI Act will be covered.

Module Name: Streamlit for ML App Development

• Learners will explore the basics of Streamlit including its layout, widgets and text elements.

• They will create and test interactive applications that showcase machine learning models and outputs.

Module Name: Deployment and MLOps

• Participants will understand the ethical considerations of deploying models, particularly in sectors like banking and e-commerce.

• They will learn to implement Responsible AI and Explainable AI principles within deployment workflows.

• The module includes hands-on training in model versioning, data monitoring and managing model registries.

Capstone Project

• Learners will work on an end-to-end project that integrates data exploration, model development and deployment strategies.

• The capstone will allow participants to demonstrate their proficiency across tools, frameworks and best practices covered throughout the program.

Assignments will be graded by industry practitioners who support participants in their learning journeys and/or by the Emeritus grading team.

This program is designed with some of the best faculty to cover relevant topics in a manner that creates positive career outcomes. Career prep modules on resumes, LinkedIn profile optimisation, job navigation and interview preparation are also provided.

We encourage our learners to complete the program to fully understand the concepts and derive valuable learning outcomes. Absent a previous approved request for deferral for the course, learners may request a full refund of all course and miscellaneous fees paid, within fourteen (14) days after course commencement. Application fees for courses are non-refundable and non-transferable. Learners who have previously been granted a course deferral are not eligible for a refund for the course. Partial (or pro-rated) refunds are not offered.

Upon successful completion of the program, you will receive a smart digital certificate. This can be shared with friends, family, schools, or potential employers. You can use it in your cover letter and resume and/or display it on your LinkedIn profile.

You will have access to the online learning platform and all the videos and program materials for 12 months following the program end date. Access to the learning platform is restricted to registered participants per the terms of the agreement.

Yes, the qualifying mark is 70% on all mandatory assignments and successfully complete the capstone project.