Artificial Intelligence and Machine Learning for Industry (Batch 05)

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

• 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 

• M2 Project: Regression application   

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

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

 LLM Alignment

• Alignment of LLM models with human expectations 

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

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.

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.

The 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 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 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.

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.

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.