Artificial Intelligence and Edge Computing Program

Program Overview

This program on ‘Artificial Intelligence and Edge Computing’ is a first level program for aspiring engineers of Edge Computing/IoT/AI developers to acquire practice-based AI/Machine Learning skills and Edge Computing fundamentals. The program has been designed with a clear vision on future developments of Edge Computing in industrial applications along with a mission of importing all essential learning fundamentals for the undergraduate engineers. The program has been weaved with necessary fundamentals of applied mathematics and python programming to inculcate the practicing skills of design and development of machine learning algorithms. The program contents are meticulously presented so that the learner will be able to visualize the applied areas of ML algorithms and to correlate & decide the preferences among Cloud, Fog and Edge Computing. The concepts on the designing of Edge Computing Systems are provided with perfect demonstrations through TensorFlow and TensorFlow lite frameworks, which provide complete learner engagement. During the program, the instructional model is designed to ensure that the learner has opportunities to explore modular tasks using retrospectives and to gain Higher order thinking skills. The program elevates the learner’s practical experience using edge computing hardware demos. The learners will also be assured with the outcomes by executing an in-program project module.

Program Objectives

Master ML Algorithms

Introduce composite relational model of edge computing along with AI, Machine Learning and IoT.

Deep Learning Expert

Impart the ML and IoT frameworks suitable for Edge Computing.

Real-world Projects

Elevate the learners with a knowledge and practice on tools supporting for ML, DL and Edge Computing solutions.

Pilot Training

Offer pilot training on modular development boards of Tiny ML.

Learning Outcomes

Understand the AI, ML and Python programming in the context of Edge Computing.

Model Python based ML solutions for simple applications.

Develop ML and IoT frameworks for EC architecture.

Defend for various ML algorithms and CNNs for their compatibility with EC.

Estimate hardware and software tools appropriate for Tiny ML.

Adapt EC based architecture for various applications of :

Civil engineering like construction management, transport engineering, naval architecture etc.
Electrical engineering like load prediction, multi-channel characterization etc., for substations.
Mechanical engineering domains like reservoir engineering, upstream sector of O&G Industry etc.

Key Highlights

Industrial Applications based on domains of Civil, Electrical and Mechanical.

General Applications of ML in Healthcare, Education, Finance etc.,

Tiny ML principles and implementation.

Instructor

Dr. Venkatalakshmi B

Subject Matter Expert – L&T EduTech

Dr. Venkatalakshmi is a seasoned researcher and technology expert with deep expertise in multisource network coding, mobile ad-hoc networks (MANETs), and optical communication. She holds a Ph.D. in Multisource Network Coding for MANETs and an ME in Optical Communication from the College of Engineering, Guindy, Anna University.

Her research spans pervasive computing, industrial IoT, AI and edge computing, RFID systems, 5G networks, mobile networking, digital signal processing, and information theory. She brings strong technical proficiency in tools such as Matlab, GloMoSim, Qualnet, ADS, Python, Power BI, Weka, and RFID API integrations.

With over three decades of experience in applied research and industry-academic collaboration, she has led initiatives in wireless sensor networks, RFID-enabled systems, and mobile computing. Dr. Venkatalakshmi has been instrumental in designing specialized curricula, setting up advanced research labs, and publishing extensively in reputed national and international journals.