Expert Faculty at Amrita Vishwa Vidyapeetham

1. Dr. P.K Giridhan – Material Science and Metallurgy, Manufacturing Processes, Learn Manufacturing, Industrial Engineering, Total Quality Management.
Currently handling course on Industrial Engineering where industrial engineering techniques like work study in which improvement in work method and standardization of time for improving productivity were discussed. Plant Layout design involving the arrangement of equipment, material flow and work flow in a work space of an industry is dealt with. Workstation design, ergonomic design of workspace, equipment and tools are considered for reducing the health risk of workers, providing job satisfaction and also improving the quality and productivity of the manufacturing process.

2. Dr. Peeta Basa Pati – Machine Learning.
About the course:
Machine Learning course provides foundational and working knowledge in the field. The students gain experience to handle various kinds of data and its prepping / transforming techniques. Foundational theoretical concepts are linked to working of the supervised and unsupervised learning techniques. Through the practical exercises, students learn to experience real world challenges and acquire expertise to effectively handle them.

3. Dr. Prashant Nair – Biometrics
Biometrics technology has become a daily part of life now because of the massive advantage it provides to its users. It has now become a must-have rather than a good-to-have. Biometric technology provides you with the empowerment of creating a unique experience for clients or end-users. The goal is to identify the perpetrator in a security setting before he or she has the chance to carry out an attack. This course provides an overview of the fundamental concepts, characteristics, and processes of biometrics. As a result, the learner can apply physiological and behavioural modalities for real world problems towards identity management system and also design multi- biometric systems by analysing the performance of various traits/indicators or identifiers for verification and recognition.

4. Dr. Gopalakrishnan E.A – Course Name: Introduction to Probabilistic Graphical Models Description:
Probabilistic reasoning is extremely essential to find solutions for problems pertinent to Data Science. In this subject, students learn about various tools and techniques related to probabilistic reasoning. Often the data available for analysis need not emerge from a deterministic system. The stochastic system of interest can be modelled as a Bayesian network and meaningful inferences can be obtained about the variables involved by observing the data. This introductory course on probabilistic reasoning helps the student to pursue further research in the upcoming area of “causal AI”.

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