Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
M.Tech in Computer Science and Engineering (CSE) with a specialization in Artificial Intelligence and Machine Learning program has been designed for students with sufficient background in computer science and engineering to develop into adept professionals. M.Tech in CSE is a graduate degree that builds skill and knowledge in advanced and current topics of computer science with a focus on AI topics. The degree is suitable for students with a bachelor’s degree in a computing-related field as well as students who want to demonstrate computer science expertise in addition to a degree in another field.
The curriculum has been designed to prepare students for highly prolific careers in the industry. Some of the job profiles include Application Analyst, Data Scientist, Data Analyst, Database Administrator, Information Systems Manager, IT Consultant, Multimedia Analyst.
Course Code | Type | Courses | L T P | Credit |
24AM601 | FC | Advanced Algorithm Design | 3 0 2 | 4 |
24MA605 | FC | Mathematical Foundations of Computing | 3 1 0 | 4 |
SC | Soft Core – I (CS) | 3 0 2 | 4 | |
SC | Soft Core – II (AI & ML) | 3 0 2 | 4 | |
24RM609 | FC | Research Methodology | 2 0 0 | 2 |
22AVP103 | HU | Mastery Over Mind (MAOM) | 1 0 2 | 2 |
23AVP601 | HU | Amrita Values Program* | P/F | |
23HU601 | HU | Career Competency I* | 0 0 3 | P/F |
Total Credits | 20 |
*Non-credit course
Course Code | Type | Courses | L T P | Credit |
SC | Soft Core – III (CS) | 3 0 2 | 4 | |
SC | Soft Core – IV (AI & ML) | 3 0 2 | 4 | |
E | Elective – I (CS) | 2 0 2 | 3 | |
E | Elective – II (AI & ML) | 2 0 2 | 3 | |
E | Elective -III (AI & ML) | 2 0 2 | 3 | |
24AM690 | FC | Case Study with Seminar | 0 0 4 | 2 |
23HU611 | HU | Career Competency II | 0 0 3 | 1 |
Total Credits | 20 |
Course Code | Type | Courses | L T P | Credit |
E | Elective – IV (CS) | 2 0 2 | 3 | |
E | Elective – V (AI & ML) | 2 0 2 | 3 | |
24AM798 | Dissertation Phase I | 10 | ||
Total Credits | 16 |
Course Code | Type | Courses | L T P | Credit |
24AM799 | Dissertation Phase II | 16 | ||
Total Credits | 16 |
Sl. No | Type | Courses | Credit |
1 | FC | Foundation Core | 12 |
2 | SC | Soft Core | 16 |
3 | E | Elective | 15 |
4 | HU | Mastery Over Mind (MAOM) / Career Competency | 3 |
5 | Dissertation | 26 | |
Total Credits | 72 |
Course Code | Courses | L T P | Credit |
24AM601 | Advanced Algorithm Design | 3 0 2 | 4 |
24MA605 | Mathematical Foundations of Computing | 3 1 0 | 4 |
24RM609 | Research Methodology | 2 0 0 | 2 |
24AM690 | Case Study with Seminar | 0 0 4 | 2 |
Course Code | Computer Science Courses | L T P | Credit |
24AM631 | Advanced Computer Architecture | 3 0 2 | 4 |
24AM632 | Advanced Computer Networks | 3 0 2 | 4 |
24AM633 | Algorithmic Graph Theory | 3 0 2 | 4 |
24AM634 | IoT and Edge Computing | 3 0 2 | 4 |
24AM635 | Embedded Programming | 3 0 2 | 4 |
24AM636 | Parallel and Distributed Data Management | 3 0 2 | 4 |
24AM637 | Cryptography and Network Security | 3 0 2 | 4 |
24AM638 | Wireless and Mobile Networks | 3 0 2 | 4 |
24AM639 | Image and Video Processing | 3 0 2 | 4 |
24AM640 | Modern Database Management Systems | 3 0 2 | 4 |
24AM641 | Software Engineering with Agile and DevOps | 3 0 2 | 4 |
24AM642 | Foundations of CyberSecurity | 3 0 2 | 4 |
24AM643 | Advanced Operating Systems | 3 0 2 | 4 |
AI & ML Courses | |||
24AM651 | Foundations of Data Science | 3 0 2 | 4 |
24AM652 | Machine Learning | 3 0 2 | 4 |
24AM653 | Computational Intelligence | 3 0 2 | 4 |
24AM654 | Deep Learning | 3 0 2 | 4 |
24AM655 | Probabilistic Graphical Models | 3 0 2 | 4 |
24AM656 | Generative AI | 3 0 2 | 4 |
Course Code | Computer Science Courses | L T P | Credit |
24AM731 | Distributed Computing | 2 0 2 | 3 |
24AM732 | Computational Geometry | 2 0 2 | 3 |
24AM733 | Full Stack Development | 2 0 2 | 3 |
24AM734 | Wireless Communication Technologies | 2 0 2 | 3 |
24AM735 | Integer Programming: Theory and Computations | 2 0 2 | 3 |
24AM736 | Blockchain Technology | 2 0 2 | 3 |
24AM737 | GPU Architecture and Programming | 2 0 2 | 3 |
24AM738 | Information Retrieval | 2 0 2 | 3 |
24AM739 | Networks and Spectral Graph Theory | 2 0 2 | 3 |
24AM740 | Design Patterns | 2 0 2 | 3 |
24AM741 | Mobile Application Development | 2 0 2 | 3 |
24AM742 | Statistical Modelling | 2 0 2 | 3 |
AL & ML Courses | |||
24AM751 | Evolutionary Machine Learning | 2 0 2 | 3 |
24AM752 | Foundations of Artificial Intelligence | 2 0 2 | 3 |
24AM753 | Reinforcement Learning | 2 0 2 | 3 |
24AM754 | Machine Learning for Big Data | 2 0 2 | 3 |
24AM755 | Applications of Machine Learning | 2 0 2 | 3 |
24AM756 | Multi Agent Systems | 2 0 2 | 3 |
24AM757 | Deep Learning for Biomedical Data Analysis | 2 0 2 | 3 |
24AM758 | Artificial Intelligence for Robotics | 2 0 2 | 3 |
24AM759 | Cloud and Big Data Analytics | 2 0 2 | 3 |
24AM760 | Quantum Artificial Intelligence | 2 0 2 | 3 |
24AM761 | Natural Language Processing | 2 0 2 | 3 |
24AM762 | Computer Vision | 2 0 2 | 3 |
24AM763 | Federated Learning | 2 0 2 | 3 |
24AM764 | Explainable AI | 2 0 2 | 3 |
24AM765 | Evolutionary Robotics | 2 0 2 | 3 |
24AM766 | Stochastic Modeling | 2 0 2 | 3 |
24AM767 | Machine Learning Ops | 2 0 2 | 3 |
* Students can take Electives from other M.Tech branches in place of any one elective. Students can select online courses in place of Elective IV and Elective V as per the university norms with the consent and approval from the department.
Duration : Two years
Eligibility Criteria
B. E. / B. Tech. (Computer Science, Information Technology, Electronics and Communication, Electrical and Electronics, Electronics and Instrumentation, Information Science), MCA, M. Sc. Computer Science, M. Sc. IT, M. Sc. Software Engineering
Biometrics
Computer Vision
Signal Processing
Data Analytics
Evolutionary Computing
Next Gen Computing
Predictive Analytics & IoT
Human Computer Interaction
Company: Robert Bosch Engineering and Business Solutions Private Limited, Coimbatore
Abstract
Air conditioning systems in large scale buildings contribute a major portion of the energy requirements. A centralized temperature monitoring system would result in the enhancement of air conditioning services in large scale buildings. Here we develop a centralized temperature monitoring scheme suitable for office environments. Wireless sensors are placed inside a compartmentalized office area, which collects the surrounding temperature data and sends it to the cloud. The application in the cloud will receive this data, store the data and present this data graphically to the end user. In order to reduce the redundant data as well as for making the sensor network energy efficient, we carry out a data analytics algorithm to identify the redundant sensors in the network based on data correlation.
Abstract
Main focus of this research work is to design an efficient and scalable RFID based hybrid indoor localization algorithm that operates over long-range RFID readers. The major objectives of this work are to design an approach that is extensible to large environments with minimal calibration and to provide high accuracy. Asset tracking is important for resource utilization and recovery. It is a service that helps locate objects instantly by providing easy access of item locations without much manual effort. We design a hybrid localization algorithm to accurately estimate the position of an object within a finite indoor space. Our approach uses power level and signal strength parameters which are readily available without the requirement of additional hardware. Furthermore, our algorithm applies intelligent region elimination techniques, thereby avoiding the use of heavy calibration and computationally complex algorithms.
Abstract
The most unbeatable technology, Internet brings to people for communication is social networks. With the exponential growth of users in internet, there is an equivalent growth among internet users to regularly visit social websites for linking with their friends, sharing thoughts, photos, videos and even discuss about their day today activities. The fact these social networks are available to all the users for free, leads to various types of security issues. Image security has been a topic of research over decades. Enhancements to individual techniques and combinations proposed till date have offered different levels of security assurances. This work aim to present a technique for secure sharing of image posts in social network. The significant feature of the scheme lies in the selection of security technique based on image content, evaluation of peers with whom the image can be shared based on text classification, transliteration and tone analysis. The proposed scheme a cost effective solution as it does not require any additional hardware. The utility of the model is demonstrated by mapping the scheme with Facebook and analyzing its performance through simulation.
At Amrita, companies vie with each other to be the early birds for hiring,
thanks to the quality of students, past and present.
5.37
7.5 LPA
25 LPA
9.5LPA
Sl.No. | Name | Roll No | Current Course | Branch | Internship/Placement | CTC |
1 | Amrita Sanjay | AM.EN.P2ARI19001 | M.Tech | Artificial Intelligence | Philips | 11 |
2 | Dulam Venkata Vijay Gopal | AM.EN.P2ARI19003 | M.Tech | Artificial Intelligence | Nokia | 6.8 |
3 | Krishna Suresh | AM.EN.P2CSE18011 | M.Tech | CSE’ | ABB | |
4 | Namitha S Nair | AM.EN.P2CSE18012 | MTech | CSE’ | Dell | 10 LPA |
Students who joined research organizations after M.Tech: Snigdha (Physics Informed Neural Networks, Mahinda University, Hyderabad)
The Computer Science MTech program in Amrita, with an emphasis on Machine Learning, was an amazing experience for me. It was refreshing to be part of an institution where you are graded based on how much you have understood rather than how much you have blindly memorized. The support and guidance I have received from the faculty were unparalleled. The encouragement to pursue genuine research is something that you don’t see often in other institutes. Opportunities such as dual degree and internships are exceptional and now that I have completed my course, I’m extremely glad that I chose to do my masters here.”
MTech course in Computer Science at Amritapuri was based on the latest technologies from the industry covering all the major aspects. Focus was on individual employability skills, personal growth and development. My learning at Amrita truly made me grow both professionally and personally in my life. Through campus placement I got placed as an intern in DELL. I was also offered a permanent job in DELL after my internship completion. I am grateful to all my faculties and Amrita University for giving me such a big platform to develop my skills.”
My internship at Intel was my first exposure to the corporate world. It was a wonderful learning experience. I honed my skills, interacted with different people and grew steadily in confidence. All this would not have been possible without the training that was imparted to us at Amrita. The constant guidance and support of my professors from Amrita paved the way for my success. Getting an offer from Intel as a software engineer was the icing on the cake.”
My 2 years of MTech at Amritapuri campus have been the defining periods of my life. During my second year of course work I got placed as an intern in GE Healthcare for 1 year which helped me in developing my technical and communication skills to a great extent. The close relation which Amrita University maintains with the industry has proved quite helpful in getting enough opportunities in terms of career. These two years of education in Amrita has shaped my thinking as a person, and I have extremely fond memories of my time here.”
The top reasons to choose Amrita for your career
Email: mtech@amrita.edu