Curriculum

Master of Technology (M.Tech) in Artificial Intelligence and Machine Learning

Artificial Intelligence and machine learning is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, neural networks, Fuzzy Logic, decision-making, expert systems, natural language processing, pattern recognition and speech recognition etc. The artificial intelligence and machine intelligence technologies bring more complex data-analysis features to existing applications. Therefore, there is a thrust in using machine learning approaches to build new solutions in business. The curriculum is designed keeping in view the need of the industry.

ELIGIBILITY CRITERIA:

  • BE/B.Tech in Computer Science and Engineering
  • BE/B.Tech in Computer Science and Engineering
  • BE/B.Tech in Information Technology
  • BE/B.Tech in Electronics and Communication Engineering with one year working experience in Industry
  • BE/B.Tech in Electrical and Electronics Engineering with one year working experience in Industry
  • BE/B.Tech in Electronics & Instrumentation Engineering with one year working experience in Industry
  • MCA / M. Sc. in Computer Science / Software Engineering /M.Sc Mathematics / Statistics

The candidates Qualified in GATE is given preference

  • Semester 1

    Credits
  • Advanced Algorithms and Analysis

    3
  • Machine Learning Techniques

    3
  • Artificial Intelligence and Neural Networks

    3
  • Statistical Modelling for Computer Sciences

    3
  • Elective I

    3
  • Artificial Intelligence and Neural Networks Lab

    2
  • Machine Learning Lab

    2
  • Semester 2

    Credits
  • Fuzzy Logic and its Applications

    3
  • Computational Intelligence

    3
  • Knowledge Engineering and Expert Systems

    3
  • Problem Solving Methods and Automated Reasoning

    3
  • Elective II

    3
  • Knowledge Engineering and Expert Systems Lab

    2
  • Computational Intelligence Lab

    2
  • Semester 3

    Credits
  • Project work- Phase I

    12
  • Semester 4

    Credits
  • Project Work –Phase II

    12
  • 62

List of Electives

  • Data Security
  • Data Mining & Applications
  • Modelling & Simulation of Digital Systems
  • Image Processing and Machine learning techniques
  • Information Retrieval
  • Pattern warehouse
  • Big Data Analytics
  • Natural language processing and AN
  • Computer Vision
  • Deep learning techniques
  • Cognitive Systems
  • Bio-Informatics
  • Number theory and Cryptography
  • Agent Systems
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