Artificial Intelligence and Machine Learning

Electrical Engineering, IIT Kanpur

Academic Programs

About the Program

Artificial Intelligence and Machine Learning – IIT Kanpur, Department of Electrical Engineering offers professionals a rigorous and practical education in AI and ML, covering foundational to advanced topics. Designed by experts from academia and industry, the curriculum ensures both theoretical depth and real-world relevance. A hands-on approach, featuring projects and case studies, enables students to apply AI/ML techniques to real-world problems, building strong analytical and problem-solving skills. Graduates of this program emerge with a deep understanding of AI and ML, ready to lead innovation across industries and research. Backed by IIT Kanpur’s academic excellence, this degree opens doors to advanced careers in the AI-driven world.

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Modules

Curriculum Template

Programme Cirriculum
Credit System Complete 12 modules 5 credits in each module Total 60 credits
Program Duration 1 to 3 Years 12 weeks in each quarter Maximum 3 modules in each quarter
Examination System Min 2 quizes Multiple Projects Final Exam
Module dependency Chart Program Structure

Faculty & Instructors



Alumni

Dr. Ketan Rajawat

Research Interest:
Optimization Algorithms, Algorithms for Big Data, Algorithms for Machine Learning and AI in Healthcare.
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Alumni

Dr. Abhishek K Gupta

Research Interest:
Wireless Communications, 5G/6G Technologies Quantum Communications, Stochastic Geometry
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Alumni

Dr. Aditya Jagannatham

Research Interest: 5G Wireless Networks, Massive MIMO Technology, mmWave MIMO Systems, NOMA, FBMC and LAA.
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Alumni

Dr. Ashutosh Modi

Research Interest: Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI).
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Alumni

Dr. Hamim Zafar

Research Interest: Intersection of Computation and Biology.
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Alumni

Dr. Priyanka Bagade

Research Interest: IoT applications in healthcare and AI/ML-based medical image analysis.
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Alumni

Dr. Tushar Sandhan

Research Interest: Applied Econometrics, Rural Economics, Empirical Finance, Commodity Markets and Consumer Behavior.
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Alumni

Dr. Vipul Arora

Research Interest: Machine Learning and Signal Processing and Optimization.
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Core

EE950

Data Analytics & Data Structures (DADS)

Elective

EE956

Machine Learning for Audio Processing

  • Prerequisite: EE950, EE951, EE952, EE954, EE955
  • Syllabus
Elective

EE957

Computer Vision

Elective

EE958

Natural Language Processing

  • Prerequisite: EE951, EE952, EE954, EE955
  • Syllabus
Core

EE951

Introduction to Linear Algebra

Elective

EE959

ML with Large Datasets

Elective

EE960

AI in IoT

Elective

EE961

AI in Healthcare

Elective

EE965

Unsupervised Learning

Elective Project

EE965

Project

  • Prerequisite: Must Completed 6 Modules
  • Syllabus
Core

EE952

Introduction to Machine Learning

Core

EE953

Basics of Optimization

Elective

EE966

AIML Projects with real-world datasets

Elective

EE967

Deep Learning and Neural Networks (DLNN) Projects with real-world datasets

Elective

EE968

Elective

EE969

Core

EE954

Deep LearningFundamentals

Core

EE955

Probability andStatistics forMachine Learning

Core

EE950

Data Analytics & Data Structures (DADS)

Core

EE951

Introduction to Linear Algebra

Core

EE952

Introduction to Machine Learning

Core

EE953

Basics of Optimization

Core

EE954

Deep LearningFundamentals

Core

EE955

Probability andStatistics forMachine Learning

Elective

EE956

Machine Learning for Audio Processing

  • Prerequisite: EE950, EE951, EE952, EE954, EE955
  • Syllabus
Elective

EE957

Computer Vision

Elective

EE958

Natural Language Processing

  • Prerequisite: EE951, EE952, EE954, EE955
  • Syllabus
Elective

EE959

ML with Large Datasets

Elective

EE960

AI in IoT

Elective

EE961

AI in Healthcare

Elective

EE965

Unsupervised Learning

Elective

EE966

AIML Projects with real-world datasets

Elective

EE967

Deep Learning and Neural Networks (DLNN) Projects with real-world datasets

Elective

EE968

Elective

EE969

Elective Project

EE965

Project

  • Prerequisite: Must Completed 6 Modules
  • Syllabus

All You Need to Know