M. Tech. in Artificial Intelligence is a program offered at School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri campus.
As we see a tech revolution in all facets of life powered by Artificial Intelligence, Amrita Vishwa Vidyapeetham offers an M. Tech. program in AI at both Amritapuriand Coimbatore campuses to provide young engineers with a futuristic edge.
M. Tech. in AI will provide the students with an opportunity to learn both foundational and experimental components of AI and Machine Learning. The program will open various career opportunities involving innovation and problem solving using Artificial Intelligence (AI) and Machine Learning (ML) technologies as well as research careers in AI and ML.
Amrita Vishwa Vidyapeetham has not appointed any Agent or Third-Party Client for securing admission in any programme. Students are hereby requested to contact only the toll-free number on our website for any admission related queries.
– Issued In Public Interest By Directorate Of Admissions And Academic Outreach
| Course Code | Type | Courses | L T P | Credit |
| 24AI601 | FC | Foundations of Artificial Intelligence | 3-0-2 | 4 |
| 24MA603 | FC | Mathematical Foundations of Computing | 3-1-0 | 4 |
| 24AI602 | FC | Machine Learning | 3-0-2 | 4 |
| SC | Soft Core – I | 3-0-2 | 4 | |
| 24RM605 | 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 – II | 3-0-2 | 4 | |
| SC | Soft Core – III | 3-0-2 | 4 | |
| SC | Elective – I | 2-0-2 | 3 | |
| E | Elective – II | 2-0-2 | 3 | |
| E | Elective – III | 2-0-2 | 3 | |
| 24AI698 | FC | Case Study | 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 | 2 0 2 | 3 | |
| E | Elective – V | 3 0 0 | 3 | |
| 24AI798 | Dissertation Phase I | 10 | ||
| Total Credits | 16 |
| Course Code | Courses | Credit |
| 24AI799 | Dissertation Phase II | 16 |
| Total Credits | 16 |
| Course Code | Courses | L T P | Credit |
| 24AI601 | Foundations of Artificial Intelligence | 3-0-2 | 4 |
| 24MA603 | Mathematical Foundations of Computing | 3-1-0 | 4 |
| 24AI602 | Machine Learning | 3-0-2 | 4 |
| 24RM605 | Research Methodology | 2 0 0 | 2 |
| 24AI698 | Case Study | 0 0 4 | 2 |
| Course Code | Courses | L T P | Credit |
| 24AI631 | Foundations of Data Science | 3-0-2 | 4 |
| 24AI632 | Advanced Algorithm Design | 3-0-2 | 4 |
| 24AI633 | Probabilistic Graphical Models | 3-0-2 | 4 |
| 24AI634 | Computational Statistics and Inference Theory | 3-0-2 | 4 |
| 24AI635 | Computational Intelligence | 3-0-2 | 4 |
| 24AI636 | Deep Learning | 3-0-2 | 4 |
| 24AI637 | Reinforcement Learning | 3-0-2 | 4 |
| 24AI638 | Mining of Massive Datasets | 3-0-2 | 4 |
| 24AI639 | Generative AI | 3-0-2 | 4 |
| Course Code | Courses | L T P | Credit |
| 24AI731 | Machine Learning for Big Data | 2 0 2 | 3 |
| 24AI732 | Applications of Machine Learning | 2 0 2 | 3 |
| 24AI733 | Evolutionary Machine Learning | 2 0 2 | 3 |
| 24AI734 | Applied Predictive Analytics | 2 0 2 | 3 |
| 24AI735 | Federated Learning | 2 0 2 | 3 |
| 24AI736 | Explainable AI | 2 0 2 | 3 |
| 24AI737 | Artificial Intelligence for Robotics | 2 0 2 | 3 |
| 24AI738 | Data Visualization | 2 0 2 | 3 |
| 24AI739 | Stochastic Modeling | 2 0 2 | 3 |
| 24AI740 | Networks and Spectral Graph Theory | 2 0 2 | 3 |
| 24AI741 | Parallel and Distributed Data Management | 2 0 2 | 3 |
| 24AI742 | Medical Signal Processing | 2 0 2 | 3 |
| 24AI743 | Computer Vision | 2 0 2 | 3 |
| 24AI744 | Natural Language Processing | 2 0 2 | 3 |
| 24AI745 | GPU Architecture and Programming | 2 0 2 | 3 |
| 24AI746 | Artificial Intelligence for IoT | 2 0 2 | 3 |
| 24AI747 | Quantum Artificial Intelligence | 2 0 2 | 3 |
| 24AI748 | Blockchain Technology | 2 0 2 | 3 |
| 24AI749 | Artificial Intelligence for Bioinformatics | 2 0 2 | 3 |
| 24AI750 | Cloud and Big Data Analytics | 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
B.E./ B. Tech. (Computer Science, Information Technology, Electronics and Communication, Electrical and Electronics, Electronics and Instrumentation ), MCA, MSc Computer Science, MSc Software Engineering
The Core courses give them sufficient expertise in the areas of Algorithm Analysis and Design, Modern Computer Architecture, Artificial Intelligence Foundations, Data Science and Machine Learning, Parallel and Distributed Data Management etc. Elective courses include various application domains of AI such as Robotics, Video/Image Analytics, Medical Signal Processing, Agents Based Systems, Data Mining and Business Analytics, Natural Language processing, Wireless Sensor Networks, Internet of things etc.
Once they complete the course, students get opportunities to get fully paid Internships and placement offers at MNCs and IT/ITES companies like Intel, Cerner, Robert Bosch, DELL etc. Also, they could publish quality research papers of the case studies/dissertations done as part of their M. Tech. program. Along with regular M. Tech, this program also provides opportunities to do Dual Degree Program (M. Tech from Amrita and MS from International universities) or One Semester/ One Year abroad programs offered by premiere universities like KTH (Sweden), Politecnico Di Milano (Italy), University of New Mexico (USA) and RWTH (Aachen University Germany).
At Amrita, companies vie with each other to be the early birds for hiring,
thanks to the quality of students, past and present.
The top reasons to choose Amrita for your career
Email
mtech@amrita.edu
