Artificial Intelligence and Machine Learning for Industry (Batch 09)

• Motivations and Introduction to Different ML Paradigms

• 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

Optimisation

• 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

• 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

Model Selection, Regularisation, and Bias-Variance Trade-off

M2 Project: Regression Application

Motivation and Introduction to Classification Problems

Logistic 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

Model Selection

Model Selection, Regularisation, and Bias-Variance Trade-off

M2 Project: Regression Application

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

IIT_Delhi_AIMLI09_Brochure.pdf

IIT_Delhi_AIMLI09_Brochure.pdf

Graph Neural Networks

• Introduction to Graph Data

• Graph Neural Network Architecture

• Hands-on Demo

Transformers

• Core mechanics — self-attention, positional encodings, causal mask

• Efficiency & fine-tuning — Flash/linear attention, LORA-FT/adapters

• Multimodal extensions - vision-language models

Generative AI

• Autoencoder, Variational Autoencoders, Generative Adversarial Networks (GANs)

• Diffusion for images and text modalities

LLM Alignment

• Alignment pipeline - SFT → reward model → RLHF/DPO/PPO

• Alternative approaches - Constitutional AI, RLAIF

• Introduction to Agentic AI Concepts: tools, memory, functions, and frameworks. (Will be covered in one lecture)

01. Linear Regression Lab

Is there a connection between sales and different types of ad expenditure? In this lab, we try to forecast the sales of a product assuming ad sales are available.

02. Logistic Regression Lab

Sentiment analysis of consumers. Can we directly infer the quality of any product based on its reviews?

03. Decision Tree, Random Forest, XGBoost

In-depth analysis of algorithms on benchmark datasets.

04. Support Vector Machines (SVM)

Image classification on fashion MNIST dataset, intuition of soft margin, hard margin, solving SVM using CVXPY.

05. Neural Networks

Basic understanding and implementation of each layer of NN. Writing and understanding gradient descent/backpropagation algorithm in Python.

Comparison of Neural Networks and SVM on image classification datasets.

06. Convolutional Neural Network (CNN)

Ever wondered how computers identify faces? We will see how CNN has revolutionised the field of computer vision.

Understanding layers, visualisation of the learning process, occlusion, and GRADCAM.

07. Sequential Model (Recurrent Neural Network/Long Short-Term Memory)

Implementation of RNN/LSTM. Hands-on implementation for caption/summary generation from images/videos.

08. Variational AutoEncoder

Understanding and implementation of Variational AutoEncoder on MNIST dataset. We will see how to encode images in a latent space of lower dimensions.

09. Generative Adversarial Networks

Is it possible to generate new images which never existed? Understanding and implementation of Generative Adversarial Networks on benchmark datasets.

10. Graph Neural Networks (GNN)

Are you ready to take your machine learning to the next level? Whether you want to build a recommender system for social media platforms or do drug prediction in biomedical, GNN has your back. We will see the extension of Deep Learning on Graphs (GNN). Introduction to several GNN variants like, GCN, GraphSage, etc.

11. Natural Language Processing

Text summarisation.

12. Generative AI

Fine-Tuning SLMs and LLMs and Their Integration with Downstream Tasks.

13. Course Project

Build your own recommender system using any of the discussed techniques (GNN, CNN, LSTM, classical ML, etc.).

IIT_Delhi_AIMLI09_Brochure.pdf

Any science, engineering, or commerce graduate/Diploma holders (10+3) or (10+2+3) are eligible to apply for this CEP, IIT Delhi AI ML course. Preference will be given to applicants with experience.

This CEP, IIT Delhi Artificial Intelligence and Machine Learning for Industry programme is designed for any fresh graduates from a science or engineering background seeking a career in the AI/ML domain, any professionals in the IT industry seeking to gain AI/ML expertise and become AI/ML specialists or any professionals seeking to upskill themselves and apply it in their strategic decision-making.

This CEP, IIT Delhi AI ML course is thoughtfully crafted with a special focus on learners from non-CS backgrounds. Contemporary case studies and practice sessions have been curated to provide hands-on experience in applying advanced machine-learning techniques to solve real-world problems.

The CEP, IIT Delhi Artificial Intelligence and Machine Learning for Industry is a Live course, and online sessions are delivered Direct-to-Device (D2D).

Yes, participants who score at least 50% marks overall and have a minimum attendance of 50% will receive a ‘Certificate of Completion’ and those who score less than 50% marks overall and have a minimum attendance of less than 50% will receive a ‘Certificate of Participation’ from CEP, IIT Delhi.

Here are some potential benefits of participating in this CEP, IIT Delhi AI and Machine Learning for Industry programme

• In-Depth Knowledge

• Practical Skills Development

• Industry-Relevant Curriculum

• Networking Opportunities

• Certification from a Prestigious Institution

• Career Advancement

• Access to Resources

• Knowledge of Industry Trends

• Opportunity for Collaborative Learning

• Access to Alumni Network

The scope of the CEP, IIT Delhi AI and Machine Learning for Industry in India is vast and transformative. With AI and ML driving innovations across sectors like healthcare, finance, e-commerce, and manufacturing, graduates are equipped to harness data-driven insights for business growth and efficiency. The programme emphasises practical applications through hands-on projects, industry collaborations, and mentorship by experts. Graduates are poised for roles in AI research, data science, consultancy, and leadership positions in companies adopting AI technologies. By leveraging, IIT Delhi's academic excellence and industry partnerships, the program prepares professionals to spearhead transformative AI initiatives in India's evolving market.

The IIT Delhi stands out as a premier choice for studying Artificial Intelligence and Machine Learning due to its esteemed faculty, cutting-edge research facilities, and industry collaborations. The programme blends theoretical foundations with hands-on practical experience, preparing students to tackle real-world challenges in AI and ML applications across diverse sectors. With access to state-of-the-art labs, collaborative projects, and mentorship from leading experts, you gain valuable skills and insights that are highly sought-after in India's tech-driven economy. By choosing IIT Delhi for the AI and ML course, you open doors to a wealth of knowledge, resources, and career prospects in this rapidly evolving field.

If a student chooses to withdraw, they can do so within 15 days from the start of the programme. In such cases, 80% of the total fee received will be refunded, excluding any applicable taxes paid. However, if the withdrawal occurs after this 15-day period, no refund will be applicable. To initiate withdrawal, students must email cepaccounts@admin.iitd.ac.in and icare@timespro.com stating their intent.