Programme Content
Fundamentals of Python*
Fundamentals of Mathematics—Linear Algebra/Probability
Measures and Descriptors of Data, Distributions and Estimation
Basics of Data Basis
Exploratory Data Analysis
Hypothesis Testing and Evaluation
Data and Information Systems
Storytelling with Data
Designing Business Dashboards
Optimisation Formulations
Gradient and Search-Based Optimisation for Machine Learning
Linear, Quadratic, and Non-Linear Programming
Multi-objective and Multi-criteria Decision-making - Evolutionary Tools
Regression and Derivatives
Trees and Random Forests
Boosting Techniques
Clustering—Hierarchical K-means Clustering
Dimensionality Reduction: PCA
Deep Feedforward Neural Nets
Convolutional Neural Nets
Long Short-Term Memory (LSTM) Networks
Introduction to Transformers and Attention Mechanisms
Explainable AI
Self-Learning Project: CNN Model for Land Use
VAEs
GANs
Diffusion Models
Introduction to Large Language Models
Applications of Generative AI
Understand Agentic AI and RAG
Self-Learning Project: Generative AI
