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The Birla Institute of Technology and Science (BITS), Pilani has launched MTech in Artificial Intelligence (AI) and Machine Learning (ML). The programme aims to help working professionals address the current and future AI potential in key sectors and thereby their career aspirations with various AI- and ML-related roles. It has been launched by the The Work Integrated Learning Programmes (WILP) division of the institute.
The programme aims to help professionals enhance their conceptual understanding and hands-on knowledge of various traditional and contemporary AI and ML techniques, including deep learning and reinforcement learning, AI application areas, such as natural language processing, computer vision, robotics and cyber security; software implementation and associated system support with applications beyond data science.
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The four-semester programme covers a wide range of skill and knowledge areas, and helps various technology professionals advance their career further, as potential ML engineers and AI scientists in the future, says the institute. The last date for applications for admissions for MTech artificial intelligence and machine learning programme is September 12.
Elaborating further about the value proposition of the MTech in AI & ML programme, Prof Anita Ramachandran, who heads the computer science and information systems group of WILP at BITS Pilani, says, “The set of core courses and electives of this programme have been carefully designed in such a manner that it aids in comprehensive development of skills and knowledge for a learner. For instance, the programme addresses the fact that the ML engineers need to be familiar with not just algorithms, such as supervised deep learning, unsupervised and reinforcement learning, but also AI application areas, such as natural language processing, computer vision, robotics, and cyber security, while staying up-to-date with the leading research in the field.”
“The programme even aims to help a learner gain better understanding of the software engineering requirements for implementing ML systems on large datasets and in resource-constrained environments — and all this, while keeping a tab on the underlying ethical issues in applying AI and ML,” Prof Ramachandran added.
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