6-Months Program - AI/ML for Industry

Registration Link and details about the programme

Course Content

Module 1: Mathematical Foundations for AI/ML

Linear Algebra for ML

• Vectors and Matrices
• Vector Space and Subspace
• System of Linear Equations
• The Concept of Rank and Independent Vectors
• Inner Product Space
• Norms
• Positive Definite Matrix
• Matrix Factorisation (EVD, SVD, QR, LR, etc.)
• Projection and Orthogonality

Probability and Statistics for Data Science

• Random Variables
• Distribution and Density Functions
• Conditional Probability
• Bayes'Theorem
• Joint Distribution
• Concept of Independence Covariance and Correlation
• Introductory Statistical Inference (Likelihood, MAP, etc.)
• Concept of Entropy
• Mutual Information and KL Divergence

Optimization

• Optimization
• Function and Derivatives
• Gradient Descent
• Stochastic Gradient Descents
• Convex Optimisation
• Formulation and Optimality Conditions
• ADAM Optimiser

Hands-on Demo 1: Linear Algebra using NumPy

• Concepts of Linear Algebra and Probability Basics
• Optimisation with Practical ML Applications

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Module 2: Regression Methods

• Simple and Multiple Linear Regression
• Hands-on Demo 2: SLR/MLR
• Least Squares Approach
• Moving Beyond Linearity: Non-linear Regression
• Hands-on Demo 3: NLR
• Model Selection, Regularisation, and Bias-Variance Trade-off

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Module 3: Classification Methods

Logsitic Regression

• Logistic Regression • Hands-on Demo 4: Logistic Regression

Decision Tree

• Introduction to Decision Trees
• Random Forests, Bagging, and Boosting
• Hands-on Demo 5: Random Forests
• Interpretability of Machine Learning Models
• Hyperplanes
• Concept of Hyperplane Classifier

SVM

• Support Vector Machines, Kernel SVM • Hands-on Demo 6: SVM
• Multi-class Classifiers

Clustering

• Clustering Methods
• Hands-on Demo 7: Clustering

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Module 4: Deep Learning

Neural Networks

• Fundamentals of Neural Network and Feedforward Network 
• Concept of Training and Backpropagation
• Hands-on Demo 8: ANN

Convolutional Neural Networks

• Fundamentals of Convolution
• Convolutional Neural Network Architecture 
• Hands-on Demo 9: CNN

Recurrent Neural Networks/LSTM

• Introduction to Time Series and Sequential Data 
• Introduction to Language Modelling and NLP
• Recurrent Neural Network and LSTM/GRU
• Hands-on Demo 10

Graph Neural Networks

• Introduction to Graph Data
• Graph Neural Network Architecture
• Hands-on Demo 11

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Extra Topics

Transformers

• Concept of Transformers and its Application to NLP

Generative AI

• Introduction to Generative AI and LLM Models Project: Deep Learning Application