M.Tech in Computer Science & Engineering (Machine Learning) programme 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. 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 industry. Some of the job profiles include: Application analyst, Data Scientist, Data analyst, Database administrator, Information systems manager, IT consultant, Multimedia analyst.
M.Tech in Computer Science & Engineering (Machine Learning) programme 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. 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 industry. Some of the job profiles include: Application analyst, Data Scientist, Data analyst, Database administrator, Information systems manager, IT consultant, Multimedia analyst.
It is a reality that that computer technology has revolutionized the modern world. Technologies that we now use for granted – Internet, mobile phones, medical technology, would not be possible without the major developments made in the field of computing.
Course Code | Type | Course Title | L | T | P | Cr. | ||
18CS601 | FC | Foundations of Computer Science Data Structures Algorithms | 3 | 0 | 1 | 4 | ||
18MA611 | FC | Mathematics for Computer Science Linear Algebra Probability and Statistics | 3 | 0 | 1 | 4 | ||
SC | Soft Core – I | 3 | 0 | 1 | 4 | |||
SC | Soft Core – II | 3 | 0 | 1 | 4 | |||
SC | Soft Core – III | 3 | 0 | 1 | 4 | |||
18HU601 | HU | Amrita Values Program* | P/F | |||||
18HU602 | HU | Career Competency I* | P/F | |||||
Total Credits | 20 | |||||||
*Non-Credit courses |
Course Code | Type | Course Title | L | T | P | Cr. | |
SC | Soft Core – IV | 3 | 0 | 1 | 4 | ||
SC | Soft Core – V | 3 | 0 | 1 | 4 | ||
Elective | Elective – I | 3 | 0 | 0 | 3 | ||
Elective | Elective–II | 3 | 0 | 0 | 3 | ||
Elective | Elective–III | 3 | 0 | 0 | 3 | ||
18RM600 | SC | Research Methodology | 2 | 0 | 0 | 2 | |
18HU603 | HU | Career Competency II | 0 | 0 | 2 | 1 | |
Total Credits | 20 | ||||||
Course Code | Type | Course Title | L | T | P | Cr. |
Elective | Elective –IV | 3 | 0 | 0 | 3 | |
Elective | Elective –V | 3 | 0 | 0 | 3 | |
18CS798 | Dissertation | 8 | ||||
Total Credits | 14 |
Course Code | Type | Course Title | L | T | P | Cr. | ||
18CS799 | Dissertation | 12 | ||||||
Total Credits | 12 | |||||||
Total Credits: 66 |
Course Code | Course Title | L | T | P | Cr. | |||
18CS621 | Foundations of Data Science | 3 | 0 | 1 | 4 | |||
18CS622 | Digital Signal and Image Processing | 3 | 0 | 1 | 4 | |||
18CS623 | Cloud and IoT | 3 | 0 | 1 | 4 | |||
18CS624 | Machine Learning | 3 | 0 | 1 | 4 | |||
18CS625 | Modeling and Simulation | 3 | 0 | 1 | 4 | |||
18CS626 | Computational Methods for Optimization | 3 | 0 | 1 | 4 | |||
18CS627 | Parallel and Distributed Data Management | 3 | 0 | 1 | 4 | |||
18CS628 | Computational Intelligence | 3 | 0 | 1 | 4 | |||
18CS629 | Modern Computer Architecture | 3 | 0 | 1 | 4 | |||
18CS630 | Deep Learning | 3 | 0 | 1 | 4 | |||
18CS631 | Advanced Algorithms and Analysis | 3 | 0 | 1 | 4 | |||
Students have to select any five soft core subjects from the list given above. |
Course Code | Course Title | L | T | P | Cr. | |||
18RM600 | Research Methodology | 2 | 0 | 0 | 2 | |||
Total Credits: 65 |
Machine Learning and Data Science Stream
Course Code | Course | L | T | P | Cr | |||
18CS701 | Machine Learning for Big Data | 3 | 0 | 0 | 3 | |||
18CS702 | Applications of Machine Learning | 3 | 0 | 0 | 3 | |||
18CS703 | Statistical Learning Theory | 3 | 0 | 0 | 3 | |||
18CS704 | Natural Language Processing | 3 | 0 | 0 | 3 | |||
18CS705 | Information Retrieval | 3 | 0 | 0 | 3 | |||
18CS706 | Data Mining and Business Intelligence | 3 | 0 | 0 | 3 | |||
18CS707 | Semantic Web | 3 | 0 | 0 | 3 | |||
18CS708 | Data Visualization | 3 | 0 | 0 | 3 | |||
18CS709 | Computational Statistics and Inference Theory | 3 | 0 | 0 | 3 | |||
18CS710 | Networks and Spectral Graph Theory | 3 | 0 | 0 | 3 | |||
High Performance Computing Stream
Course Code | Course | L | T | P | Cr |
18CS731 | Parallel and Distributed Computing | 3 | 0 | 0 | 3 |
18CS732 | GPU Architecture and Programming | 3 | 0 | 0 | 3 |
18CS733 | Reconfigurable Computing | 3 | 0 | 0 | 3 |
18CS734 | Data Intensive Computing | 3 | 0 | 0 | 3 |
18CS735 | Fault Tolerant Systems | 3 | 0 | 0 | 3 |
18CS736 | Computer Solutions of Linear Algebraic Systems | 3 | 0 | 0 | 3 |
Live-in-Labs
18CS737 | Live-in-Labs | 3 | ||||||
Students can do Live-in-Labs course in lieu of an elective from II Semester or III Semester. |
Networks and IoT Stream
Course Code | Course | L | T | P | Cr |
18CS721 | Sensor Networks and IoT | 3 | 0 | 0 | 3 |
18CS722 | Predictive Analytics for Internet of Things | 3 | 0 | 0 | 3 |
18CS723 | Wireless Sensor Networks | 3 | 0 | 0 | 3 |
18CS724 | Wireless and Mobile Networks | 3 | 0 | 0 | 3 |
18CS725 | Pervasive Computing | 3 | 0 | 0 | 3 |
18CS726 | IoT Protocols and Architecture | 3 | 0 | 0 | 3 |
Computer Vision Stream
Course Code | Course | L | T | P | Cr |
18CS711 | Video Analytics | 3 | 0 | 0 | 3 |
18CS712 | Medical Signal Processing | 3 | 0 | 0 | 3 |
18CS713 | Content Based Image and Video Retrieval | 3 | 0 | 0 | 3 |
18CS714 | Pattern Recognition | 3 | 0 | 0 | 3 |
18CS715 | 3D Modeling for Visualization | 3 | 0 | 0 | 3 |
18CS716 | Computer Vision | 3 | 0 | 0 | 3 |
18CS717 | Visual Sensor Networks | 3 | 0 | 0 | 3 |
18CS718 | Image Analysis | 3 | 0 | 0 | 3 |
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
Two years
Duration : Two years
. Biometrics
. Computer Vision
. Signal Processing
. Data Analytics
. Evolutionary Computing
. Next Gen Computing
. Predictive Analytics & IoT
. Human Computer Interaction
Smart Climate Monitoring for Large Scale Buildings
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.
Hybrid Localization Algorithm for Indoor Asset Tracking
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.
Securing Image Posts in Social Networking
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.
Excellent placement record of 88 % for the eligible students combining all three campuses in 2021 Batch
Students who are eligible and opt for placements could potentially have multiple job offers.Placements have always been excellent at Amrita. An array of companies visit Amrita for placement of M.Tech Students.Corporate & Industry Relations, has developed industry-academia association through frequent visits, interactions with the top management and facilitation of Faculty Development Programmes, Student Visits, Industrial Training, and Project Guidance under Corporate Action Plan.
Fully equipped computer laboratory exclusively for PG students Amrita Multi Dimensional Data Analytics Laboratory to support projects in Pervasive computing, Big Data Analytics, Web Science etc
Cognizant Innovation lab focusing on Robotics, Artificial Intelligence and Security, Image Analysis, Video processing and computer vision.
The top reasons to choose Amrita for your career
“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.”
At Amrita, companies vie with each other to be the early birds for hiring,
thanks to the quality of students, past and present.