Our M.Tech programme prepares you to take up industrial job, research and development or academia.
The 2021 M.Tech programs are on track and slated to start on time
All Programs are AICTE Approved
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.
This M.Tech programme gives a specialized focus on areas of technology, aiming to develop skills and career prospects. The master's degree program offers an integrated course of study covering the theory, implementation and design of information, computing, communication and embedded systems.
This programme has specialized courses in the streams of Data Science, Computer Vision, IoT and High Performance Computing with significant focus on research. As a part of the programme during the period of study, students have the opportunity to intern at leading companies and R&D labs for a period of 6 months to one year. There are opportunities for the students to take up a semester or one year study at International Universities like Virje University, Netherlands, UC Davis, UNM for an exchange programme or to pursue a dual degree programme.
Graduates of this programme are well represented in Oracle, IBM, HP, Cerner, Intuit, and other major MNCs as well as in research in premier academic institutions in India and abroad. The graduates are competent to take up R&D positions in Industry, academia and research laboratories.
Duration : Two years
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.
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 |
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 | |||
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 |
18CS737 | Live-in-Labs | 3 | ||||||
Students can do Live-in-Labs course in lieu of an elective from II Semester or III Semester. |
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 | |||
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 |
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.
Over 200 companies visit the campus every year.
Dream option available for placed students to go for higher packages / better job profiles.
More than 400 industrial tie-ups.
Highest CTC/Salary: Rs. 75 Lakhs
Placement and six month Internships at Multinational Companies such as Google, Microsoft, Intel, Cisco, ABB, Wipro, Alcatel-Lucent, Cerner, Bosch, Honeywell, TCS, Zoho etc.
M. Tech. Computer Science Engineering 2017-2019 Batch
The students of M. Tech. Computer Science and Engineering, 2017-2019 batch, secured prestigious placements at the following companies:
Center for International Programmes facilitates foreign internships with scholarship and higher education. Students can even opt for dual degree programmes.
Important: Direct admission and funding eligibility to partner International Universities in US, Europe to earn Dual Degrees- M. Tech. & M. S. in 2 years.
Internships: M. Tech. 2018-2020 Batch
The students of M. Tech. Computer Science and Engineering, Semester 2, 2018-2020 batch, secured prestigious internships at the following companies:
Our M.Tech programme prepares you to take up industrial job, research and development or academia.
I chose M Tech at Amritapuri campus as it has the best infrastructure and resources. The programme helped me grow personally and professionally.
Its a great school with the best infrastructure, highly qualified faculty and a wonderful student community.
Visit Department of Computer Science and Engineering (Amritapuri Campus) Website
Visit Department of Computer Science and Engineering (Bengaluru Campus) Website
Visit Department of Computer Science and Engineering (Coimbatore Campus) Website
If you wish to know more about the course please mail to,
mtech@amrita.edu