Executive-Friendly Program | High Impact Format

Key Highlights of IIT Kanpur Masters in
AI and Machine Learning

  • SelectionBased on academic and professional background and test, interview where necessary. No GATE required.
  • High Impact Format Weekend-only Live interactive sessions coupled with self-paced learning.
  • Executive Friendly Schedule Learn while you earn, with the flexibility to complete the program between 1 - 3 years.
  • Career Advancement and Networking Support for placement and facilitation of incubation at IIT Kanpur's Incubation Centre.
  • IIT Kanpur Alumni Status Become an IIT Kanpur alumni with access to all the alumni privileges.
  • Credits Transfer Waiver of upto 60 credits for higher education (MTech/PhD) at IIT Kanpur.

Admission Process

  • Application
    Register with Mobile Number
    Submit Details
    Remit Application Fee
    Upload Documents
  • Selection
    Application Review
    Selection Test *
  • Admission

Class Start - January 2025

* Selection test differs for every programme

e-Masters in Artificial Intelligence and Machine Learning Overview

IIT Kanpur's Masters in Artificial Intelligence and Machine Learning program offers an exceptional opportunity for individuals to attain expertise in this dynamic field. The program's well-structured curriculum and practical approach ensure a comprehensive understanding of the subject. Our Masters in AI and ML program covers various topics, from foundational principles to advanced concepts, preparing students to be at the forefront of advancements. The program is designed by experts in the industry and academia, ensuring its relevance and rigor.

A distinguishing feature of the program is its hands-on approach. Through a series of projects and case studies, students gain practical experience in applying AI and ML techniques to real-world scenarios. This approach enhances analytical and problem-solving skills, making graduates adept at tackling career challenges.

Pursuing an e-masters in Artificial Intelligence and ML from IIT Kanpur signifies a commitment to academic excellence and professional growth. Graduates emerge with a deep understanding of AI and ML, poised to contribute meaningfully to industries and research endeavors.

IIT Kanpur's reputation for academic rigor and technological contributions makes this program highly regarded. By enrolling in our Masters in Artificial Intelligence and Machine Learning degree, individuals open doors to intellectual advancement, skill refinement, and influential roles in shaping the AI landscape.

Graduation Ceremony at IIT Kanpur Campus

Outcomes

  • Empower your career with AI expertise. Be part of
    deeptech projects in your organization
  • Get an eMasters Degree
    from IIT Kanpur
  • Become a part of IIT Kanpur's
    alumni network
  • Learn from a leading research
    faculty group
  • Receive mentorship and career support from
    the IIT Kanpur placement cell
  • Incubation support for promising
    startup initiatives
  • Opportunity to forge a meaningful network
    with diverse professionals

Faculty

Acquire knowledge from accomplished faculty members driving advancements in Artificial
Intelligence and Machine Learning.

  • Ketan Rajawat
    Ph.D., University of Minnesota, USA
    Associate Professor, Department of Electrical Engineering, IIT Kanpur
    Chief, SPiN Research Group Lab, IIT Kanpur
    Research Expertise: Optimization Algorithms, Algorithms for Big Data, Algorithms for Machine Learning, AI in Healthcare
  • Tushar Sandhan
    Ph.D., Seoul National University, South Korea
    Assistant Professor, Department of Electrical Engineering, IIT Kanpur
    Research Expertise: Computer vision, Machine learning, Robotics
  • Priyanka Bagade
    Ph.D., Arizona State University, USA
    Assistant Professor, Department of Computer Science and Engineering, IIT Kanpur
    Research Expertise: IoT, Sensors, Mobile Computing, Deep Learning
  • Vipul Arora
    Ph.D., IIT Kanpur
    Associate Professor, Department of Electrical Engineering, IIT Kanpur
    Adjunct Faculty, Samtel Centre for Display Technologies and Flexible Electronics
    Chief, MADHAV Lab, IIT Kanpur
    Research Expertise: Audio signal processing, Machine Learning, Music Information Retrieval, Speech Recognition
  • Ashutosh Modi
    Ph.D., Saarland University, Germany
    Assistant Professor, Department of Computer Science and Engineering, IIT Kanpur
    Research Expertise: Natural Language Processing, AI, Machine Learning, Deep Learning, Legal AI, Affective Learning
  • Abhishek K Gupta
    Ph.D., University of Texas, Austin - USA
    Assistant Professor, Department of Electrical Engineering, IIT Kanpur
    Chief, Modern Wireless Networks Group, IIT Kanpur
    Research Expertise: Wireless Communications, 5G/6G Technologies Quantum Communications, Stochastic Geometry
  • Hamim Zafar
    Ph.D, Rice University, Houston, U.S.A
    Assistant Professor, Department of Computer Science and Engineering & Department of Biosciences and Bioengineering, IIT Kanpur
    Research Expertise: Computational biology; Probabilistic Modeling; Single-cell Biology; Evolution and Cancer
  • Aditya Jagannatham
    Ph.D. in Electronics and Communications Engineering at University of California
    5G Wireless Networks, Massive MIMO Technology, mmWave MIMO Systems, NOMA, FBMC and LAA.

e-Masters Degree in Artificial Intelligence and Machine Learning Curriculum

Experience a robust curriculum meticulously crafted by IIT Kanpur's experts in the e-Masters in Artificial Intelligence and Machine Learning. This program merges theoretical depth with practical exposure, ensuring proficiency in AI and ML by delving into real-world applications.

Modules

The eMasters Artificial Intelligence and Machine Learning (AI/ML) encompass a comprehensive curriculum thoughtfully divided into 5 Core Modules and 7 Elective Modules. The modules have been meticulously curated to provide participants with appropriate exposure to AI and Machine Learning and their impact on the changing technological landscape.

  • Data Analytics and Data Structures
  • Introduction to Linear Algebra
  • Introduction to Machine Learning
  • Basics of Optimization
  • Deep Learning Fundamentals

  • Probability and Statistics for Machine Learning
  • ML for Audio Processing
  • Computer Vision
  • Natural Language Processing
  • ML with Large Datasets
  • AI in IoT
  • AI in Healthcare
  • Industrial AI / Automation
  • Reinforcement Learning
  • Unsupervised Learning
  • AIML Projects with real-world datasets
  • Deep Learning and Neural Networks (DLNN) Projects with real-world datasets
  • Projects

Detailed Curriculum

Immersive Learning Format

  • Live Interactive Sessions & Guest Lectures
  • Case study-based Learning
  • Projects
  • Periodic Assessments
  • Online Examination
  • Campus Visit
  • Online LIVE and self-paced sessions are delivered through AI-powered iPearl.ai
  • Weekly/ Bi-weekly live interaction as per the faculty availability
  • Apply learnings through projects while working in teams and establish a peer network
  • Final module-level exams will be conducted online
  • Opportunity to meet experts and experience the IITK campus during campus visits

Eligibility

  • Eligibility criteria: Bachelor's Degree (4 years program) or a Masters Degree in the relevant discipline (Computer Science/Information Technology/MCA, etc) with at least 55% marks or 5.5/10 CPI.
  • Minimum of 2 years of full time work experience (You need not be currently employed to be eligible).

Masters in AI and ML: Fees

Application fee ₹1500 (to be paid during application submission)

Fee structure for candidates opting to complete the program in 1 year.

Details Amount
Registration Fee
To be paid within 1 week of selection		 ₹40,000
Admission Fee
To be paid to complete enrollment		 ₹1,60,000
Module Fee
To be paid at the beginning of every quarter based on no. of modules selected
(Total 12 Modules) 		₹5,40,000
₹45,000 per module
Quarter Fee*
To be paid at the beginning of every quarter		 ₹60,000
₹15,000 per quarter
Total Fee ₹8,00,000

*For every additional quarter, fees of Rs 15,000 will be applicable.

For Example

Candidates opting to complete the program in 5 quarters need to pay an additional fee of ₹15,000

Candidates opting to complete the program in 11 quarters need to pay an additional fee of ₹1,05,000

All other fees remain the same.

Fees paid are non-refundable(after a certain time period) and non-transferable.

About IIT Kanpur

Established in 1959 by the Government of India, Indian Institute of Technology Kanpur (IIT Kanpur) is a globally acclaimed university for world-class education and research in science, engineering, management and humanities. We aim to provide leadership in technological innovation for the growth of India.

  • Ranked 5th in Innovation, 4th in Engineering and 4th in Overall Category by NIRF 2024
  • Built on world-class academic research culture
  • Offers various undergraduate, post-graduate, integrated, and research programs in the field of engineering, science, management, and design
IIT Kanpur Online Masters Degree Courses

State-of-the-Art Digital Learning Platform

The eMasters Program by IIT Kanpur will be delivered on iPearl.ai, a State-of-the-Art digital learning platform, powered by TalentSprint. iPearl.ai, highly rated for its user experience, is a direct-to-device platform that works seamlessly on any internet-connected device and provides a single-sign on experience for all your learning needs including recorded videos, reading material, live interactive sessions, assignments, quizzes, discussion forums, virtual lounges and more.

Frequently Asked Questions

Core Modules

  • Data Analytics and Data Structures: Basic programming and scripting, data I/O, data visualization, data structures
  • Introduction to Linear Algebra/ Linear Algebra for ML: Vectors, matrices, SVD, solving Ax=b, matrix calculus, Eigenvalue decomposition
  • Introduction to Machine Learning: classification, regression, reinforcement learning, linear models, supervised and unsupervised learning, time-series, parameter tuning
  • Basics of Optimization: local vs. global optimum, gradient, role of convexity, gradient descent, constrained optimization, stochastic gradient descent
  • Deep Learning Fundamentals: neural networks (NN) and deep learning, artificial NN, optimization algorithms, CNNs, RNNs, VAE, GAN, transfer learning

Elective Modules

  • Probability and Statistics for Machine Learning: Introduction to Probability, Random Variables, distributions, examples of distributions, statistics, confidence intervals
  • ML for Audio Processing : introduction to speech and music, DSP, audio classification, automatic speech recognition, music information retrieval, audio search
  • Computer Vision: Overview of Computer Vision, Image Processing Operations, Image Classification/Segmentation, Object Recognition and Detection, Object Tracking, Image Generation, Video Analysis, Facial Recognition, Human Pose Estimation, Medical Image Analysis, Autonomous Vehicles
  • Natural Language Processing: NLP basics, language models, tagging parsing, named entity recognition, coreference resolution, text classification, distributional semantics, distributed representations, word vectors, deep models, sequence-to-sequence models, transformers, graph NNs
  • ML with Large Datasets: distributed computing, databricks, spark visualization, dimensionality reduction, distributed regression, kernel approximation, deep learning, distributed learning, neural architecture search, model compression
  • AI in IoT: New Trends and Applications, IoT Architecture, Middleware, Fog Computing, Sensors and Actuators, IoT Communications and Sensor Networks, IoT Security, AI in IoT
  • AI in Healthcare: AI and its impact on Healthcare, visual data analysis in medical domain, rule-based diagnosis, healthcare records processing, robotics in healthcare, digital twins
  • Industrial AI / Automation: Robotics and Automation, Industry 4.0, Future Trends and Emerging Applications
  • Unsupervised Learning: Introduction and K- means Clustering, Hierarchical and Spectral Clustering, Dimension Reduction-Linear and Nonlinear, Matrix Factorization, Graphical Models, Mixture Models and EM, Approximate Inference
  • AIML Projects with real-world datasets: Introduction to PYTHON, ML packages, Data compression, Principal Component Analysis (PCA), Linear Regression, Logistic Regression, Support Vector Machines, Naïve Bayes, Linear Discriminant Analysis, Decision Tree Classifiers (DTC), K-means and Probabilistic Clustering
  • Deep Learning and Neural Networks (DLNN) Projects with real-world datasets: Students will complete PYTHON (TensorFlow and Keras)-based projects on key Deep Learning/Neural Network techniques, covering topics like Neurons, Activation Functions, Deep Neural Networks, and Convolution Networks. These projects will use real-world datasets, including Boston Housing Price, California Housing Price, Mobile Phone, Fashion, Handwritten Digit Classification, and IMDB movie rating datasets. The program also focuses on Neural Unit Structures, Neuron Properties, Back Propagation, Convolution, Pooling, and Flattening operations. Students will gain skills in PYTHON IDEs and advanced packages like TensorFlow and Keras for future DL/NN projects.
  • Projects

View all