IIT Delhi Artificial Intelligence and Machine Learning

Course Description

Program overview:

Artificial Intelligence (AI) and Machine Learning (ML) is a significant evolution in computer science and data processing that is not only rapidly revolutionising industries and businesses but also spawning new business processes and models. AI and ML help in improving production efficiency and capacity and eliminate primary losses, and many other expenses. AI and ML applications also enable organisations to extract value from the data they collect, deliver business insights, automate tasks, and advance system capabilities. Data is the new lifeblood of businesses. Technologies are enabled to retrieve and store data from every customer touchpoint and aspect of the company so that it can be used to study consumer behavior and find solutions to various business problems. The IIT Delhi AI and Machine Learning for Industry programme aims to equip participants to intelligently apply the techniques of Machine Learning to complex issues in various domains ranging from medical diagnostics to sports analytics.

Programme Timelines

Last Date to Apply	30th August, 2024
Programme Start Date	15th September 2024
Programme End Date	15th March 2025

 

Course Fee:  ₹ 1,69, 000 + Taxes

Class Timings: Saturday: 09:00 a.m. – 12:00 p.m.

Duration:  The duration of this IIT Delhi AI and ML course is 6 Months

Learning Hours - 230 hours

80 hours of online live sessions

30 hours of self-paced Python and Data Analysis Boot Camp

92 hours quizzes/ assignment/ projects/ recordings

10 hours international guest lectures by industry experts

6 hours campus immersion (optional)

12 hours extra doubt clearing sessions

Who Should Attend?

Fresh graduates from science or engineering background seeking a career in the AI/ML domain.

Professionals in the IT industry seeking to gain AI/ML expertise and become AI/ML specialists.

Professionals seeking to upskill themselves and apply it in their strategic decision-making.

Eligibility Criteria

Graduates in science, technology, engineering, mathematical sciences and management are eligible for this IIT Delhi AI and ML course.

Admission Criteria

The selection for this IIT Delhi AI ML course will be based on application review.

Certificate and Assessment

Assessment and Evaluation

20% - End of module MCQ based exam

20% - End of module projects

30% - End of programme MCQ based exam

30% - End of program project

Attendance (Grace) – 5%

Certification

Candidates who score at least 50% marks overall and have a minimum attendance of 50%, will receive a ‘Certificate of Completion’.

Candidates who score less than 50% marks overall and have a minimum attendance of 50%, will receive a ‘Certificate of Participation’.

The organising department of this programme is Yardi School of Artificial Intelligence, IIT Delhi.

Sample Certificate

*Only e-certificates will be issued by CEP, IIT Delhi for this programme.

How It Works

Step1: Select course of your interest and register.

Step2: Receive Counselling from our Programme Advisors.

Step3: Get your documents verified and give an Interview if applicable.

Step4: Obtain Offer Letter and Give your Acceptance.

Step5: Pay Preliminary Course Fee.

Step6: Complete Onboarding and commence Course.

 

Key Highlights of Program

IIT Delhi is ranked #2 as per QS World University Rankings 2024 in India.

Sessions on Generative AI and LLM Models.

Contemporary case studies and hands-on practice sessions.

International guest lectures by industry experts.

Doubt clearing sessions.

E-certificate issued by CEP, IIT Delhi.

80 hours of live online lectures by IIT Delhi faculty.

 Learning Outcomes

After the end of this IIT Delhi AI ML course, learners will be able to:

Develop an understanding of machine learning tools, algorithms, and industrial applications

Gain hands-on experience in applying advanced ML techniques through case studies and practice exercises 

Understand the working of neural networks and gain the ability to design and implement them using various tools and techniques

Be able to design and implement various AI and ML techniques in a range of real-world applications

Learn latest topics such as Generative AI

Pedagogy

The teaching approach of this IIT Delhi AI and ML course will be highly interactive taking advantage of the technological benefits. The pedagogy followed for the programme will be a judicious blend of lectures, case studies, hands-on demo, assignments and projects.

Programme Delivery

Live Online Sessions delivered Direct-to-Device (D2D).

Campus Immersion

An offline 1-day campus immersion for interaction between faculty and learners in IIT Delhi campus (optional for learners to attend).

Job Roles

Below are the job roles available in this field :

AI Architect: Designs and oversees the implementation of AI solutions, ensuring they align with organisational goals and integrate seamlessly with existing systems.

Machine Learning Engineer: Develops and deploys machine learning models, focusing on optimising algorithms and managing data pipelines for model training and evaluation.

Data Scientist: Analyses and interprets complex data sets to extract actionable insights using statistical techniques and machine learning to inform business decisions.

AI Engineer: Builds and maintains AI systems and applications, combining expertise in software engineering and machine learning to develop functional AI-driven products.

Course Content:

Module 0: Practical Python for Industry Professionals

Foundations of Python Programming

 

Module 1: Mathematical Foundations for AI/ML

Motivations and Introduction to different ML Paradigm

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

Learning outcomes:

Develop a comprehensive understanding and application of linear algebra concepts, probability and statistics, and optimisation in real-world machine learning tasks.

 

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

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

Project

Regression application

Learning outcomes:

Master simple and multiple linear regression, non-linear regression, and the least squares approach, gaining practical experience through hands-on demos. Additionally, learn model selection, regularisation, and the bias-variance trade-off, culminating in a regression application project discussion.

 

Module 3: Classification Methods

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

Project

Classification Application

Learning outcomes:

Learners will develop expertise in logistic regression, decision trees, random forests, and support vector machines, gaining practical experience through hands-on demos. They will also learn clustering methods and the interpretability of machine learning models, culminating in a classification application project discussion.

 

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

Transformers

Concept of Transformers and its Application to NLP

Generative AI

Introduction to Generative AI and LLM Models

Project

Deep learning application

Learning outcomes:

Master the fundamentals of neural networks, including feedforward networks, training, and backpropagation, with practical experience through hands-on demos. Additionally, learn advanced topics such as convolutional neural networks, recurrent neural networks, graph neural networks, transformers, and generative AI, applying these concepts to real-world applications.

Projects

1. 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.

2. Logistic Regression Lab

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

3. Decision Tree, Random Forest, XGBoost

In-depth analysis of algorithms on benchmark datasets.

4. Support Vector Machines

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

5. Neural networks

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

6. Neural Networks

Comparison of Neural Networks and SVM on Image classification datasets.

7. Convolutional Neural Networks

Understanding layers, visualisation of the learning process, Occlusion, GRADCAM

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

Implementation of RNN/LSTM. Hands-on implementation for Caption/Summary generation from images/videos.

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

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

11. Graph Neural Network

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 GCN, GraphSage, etc

12. Natural Language Processing

Text Summarisation

13. Course Project

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

 

Dates and Fees

Programme Fee

Particulars	Amount (in ₹)
Programme Fee	1,69,000
GST @18%	30,420
Total Fee	1,99,420

Note:

All fees should be submitted in the IITD CEP Account only, and the details will be shared post-selection.

Withdrawal & Refund from Programme

Candidates can withdraw within 15 days from the programme start date. A total of 80% of the total fee received will be refunded. However, the applicable tax amount paid will not be refunded on the paid amount.

Candidates withdrawing after 15 days from the start of the programme session will not be eligible for any refund.

If you wish to withdraw from the programme, you must email cepaccounts@admin.iitd.ac.in and icare@timespro.com stating your intent to withdraw. The refund, if applicable, will be processed within 30 working days from the date of receiving the withdrawal request.

Instalment Schedule

Particulars		Amount (₹)**
Registration Fee	To be paid at the time of registration	10,000
1st Instalment	Within one-week of offer-rollout	59,000
2nd Instalment	12th November, 2024	50,000
3rd Instalment	27th December, 2024	50,000

** GST @18% will be charged extra in addition to the fee.

Easy EMI Options Available

Note:

Registration Fee of ₹10,000 will be charged for processing the selected applications only, post confirmation email from the institute. The registration fee is also part of the total programme fee.

An offer letter from CEP, IIT Delhi will be released post the successful receipt of the Registration Fee.

Payment of fees should be submitted in the IIT Delhi CEP account only and the receipt will be issued by the IIT Delhi CEP account for your records.

Loan and EMI Options are services offered by TimesPro. IIT Delhi is not responsible for the same.

 

Frequently Asked Questions (FAQs)

 

What is the IIT Delhi Artificial Intelligence and Machine Learning for Industry programme?

The IIT Delhi AI and Machine Learning for Industry is a comprehensive course that provides participants with a foundational understanding of machine learning tools, algorithms, and their industrial applications. The course will equip participants with the knowledge and practical skills necessary to proficiently apply machine learning techniques to tackle complex problems across diverse domains such as sales and marketing, medical diagnostics, and sports analytics.

What are the eligibility criteria for the IIT Delhi Artificial Intelligence and Machine Learning for Industry programme?

Any graduate in science, technology, engineering, mathematical sciences, and management is eligible to apply for this IIT Delhi AI ML course.

Who is this IIT Delhi AI ML course suitable for?

This IIT Delhi AI ML course 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.

How is the IIT Delhi AI and Machine Learning for Industry programme structured?

This 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.

Is the IIT Delhi Artificial Intelligence and Machine Learning for Industry live or recorded?

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

Will I get a certificate after the completion of this IIT Delhi AI and ML course?

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.

What are the benefits of participating in this IIT Delhi Artificial Intelligence and Machine Learning for Industry programme?

Here are some potential benefits of participating in this 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

What is the scope of the IIT Delhi Artificial Intelligence and Machine Learning for Industry in India?

The scope of the 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.

Why should you choose IIT Delhi for the Artificial Intelligence and Machine Learning course?

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 program 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.

What is the refund policy if a student needs to withdraw from the IIT Delhi Artificial Intelligence and Machine Learning for Industry course?

If a student chooses to withdraw, they can do so within 15 days from the start of the program. 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.

 

 

Reference taken from: https://timespro.com/executive-education/iit-delhi-ai-and-machine-learning-for-industry