M.Tech (Artificial Intelligence)

Post Graduate Programmes

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Program Code 101448

Course Overview

  • Campus
  • Noida
  • Institute
  • Amity School of Engineering and Technology
  • University
  • Amity University Uttar Pradesh
  • Program Code
  • 101448
  • Eligibility
  • For Non Sponsored category:

    Bachelor Degree of 4 year Engg. in (CSE/IT/ECE/EEE/EE/E&TE/AI/ME/Instrumentation & Control Engg./ Bioinformatics) agg.60% or AMIE / AMIETE in (CS/IT) agg. 60% or MCA or Master's Degree in (CS/IT/Maths/Physics/Statistics) agg. 60%

    AND

    Class XII (agg.60%) with PCM

    For Sponsored category:

    Eligibility will be relaxed by 5% in bachelor's degree and class XII for Sponsored category.

     

     

  • Duration
  • 2 years
  • 1st Year Non Sponsored Semester Fee (Rs. in Lacs)
  • 0.72
  • 1st Year Sponsored Semester Fee (Rs. in Lacs)
  • 1.08
 
  • Fee Structure
  • Program Educational Objectives
  • Program Learning Outcomes
1st Year Non Sponsored Semester Fee (Rs. in Lacs) : 0.72
1st Year Sponsored Semester Fee (Rs. in Lacs) : 1.08
  • The students shall have the ability to apply knowledge of science, engineering & technology to design and develop innovative products through research and provide solutions as per industry and societal requirements.
  • The students shall have the ability to apply research knowledge and methods to solve engineering problems.
  • Demonstrate skills as an Artificial Intelligence professional and perform with Ethical and Moral values.
  • Engage in active research for Professional development in the field of Artificial Intelligence with an attribute of lifelong learning.
  • Carryout consultancy and extension activity either as a member of team or as an individual.
  • Graduates will be able to apply the knowledge of mathematics, science, engineering fundamentals, and domain knowledge of Computer Science and Engineering to the solution of complex engineering problems.
  • Graduates will be able to identify, formulate and solve complex engineering problems reaching substantiated conclusions with focus in Computer Science and Engineering.
  • Graduates will be able to create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations with focus in Computer Science and Engineering
  • Graduates will be able to design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations
  • Graduates will be able to communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
  • Graduates will be able to function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  • Graduates will be able to apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
  • Graduates will follow ethical principles/norms and commit to professional ethics and responsibilities/ norms of engineering practice
  • Graduates will be able to demonstrate knowledge and understanding of the Computer Science and Engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments
  • Graduates will recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
  • Graduates will be able to understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
  • Graduates will be able to use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.

Course Structure

  • 1st Year
  • 2nd Year

Semester 1

Course Title Course Type Credit L T PS SW Total Credits Syllabus
Advanced Data Structures (PG) Core Courses 0 0 0 0 0.00 View
Mathematical Foundation of Computer Science (PG) Core Courses 0 0 0 0 0.00 View
Advanced Computer Graphics (PG) Specialisation Elective Courses 3 0 2 0 4.00 View
Advanced Software Project Planning and Management (PG) Specialisation Elective Courses 3 0 4 0 5.00 View
Advanced Software Testing and Quality Assurance (PG) Specialisation Elective Courses 3 0 4 0 5.00 View
Data Warehousing and Data Mining (PG) Specialisation Elective Courses 3 0 4 0 5.00 View
High Performance Computer Architecture (PG) Specialisation Elective Courses 3 1 0 0 4.00 View

Semester 2

Course Title Course Type Credit L T PS SW Total Credits Syllabus
Advanced Soft Computing (PG) Core Courses 3 0 4 0 5.00 View
Machine Learning and Big Data Analytics (PG) Core Courses 3 0 4 0 5.00 View
Optimization Methods for Machine Learning (PG) Specialisation Elective Courses 3 0 4 0 5.00 View
Industrial Internet of Things (PG) Specialisation Elective Courses 3 0 4 0 5.00 View
Pattern Recognition and Image Processing (PG) Specialisation Elective Courses 3 0 4 0 5.00 View
Probabilistic Graphical Models (PG) Specialisation Elective Courses 3 0 2 0 4.00 View
Independent Study and Research - I (PG) Non Teaching Credit Courses 0 0 0 0 2.00 View

Semester 3

Course Title Course Type Credit L T PS SW Total Credits Syllabus
Artificial Neural Networks (PG) Specialisation Elective Courses 2 0 2 0 3.00 View
Natural Language Processing Algorithm and Applications (PG) Specialisation Elective Courses 2 0 2 0 3.00 View
Deep Learning Algorithms and Applications (PG) Specialisation Elective Courses 2 0 2 0 3.00 View
Reinforcement Learning Algorithms and Applications (PG) Specialisation Elective Courses 2 0 2 0 3.00 View
Summer Internship (PG) Non Teaching Credit Courses 0 0 0 0 4.00 View
Minor Project (PG) Non Teaching Credit Courses 0 0 0 0 4.00 View
Industry 4.0 and Industrial Internet of Things (PG) MOOC (Amity On - line / NPTEL / SWAYAM / Future Learn) 0 0 0 0 0.00 View
Information Retrieval System (PG) Specialisation Elective Courses 3 1 0 0 4.00 View
Applied Accelerated Artificial Intelligence (PG) MOOC (Amity On - line / NPTEL / SWAYAM / Future Learn) 0 0 0 0 0.00 View

Semester 4

Course Title Course Type Credit L T PS SW Total Credits Syllabus
Dissertation (PG) Non Teaching Credit Courses 0 0 0 0 16.00 View