Program Overview
With the paradigm shift towards Digital Transformation in industries, there exists a huge volume of digital data in cloud storage about the Men, Materials, and Machines of the organization. This data holds valuable information that can be used for process planning, predictive failures, and business optimization.
This program aims to equip learners with strategic principles of Artificial Intelligence theory to extract such information from the available data. As AI's reach consistently grows, so do the programming features. The program introduces appropriate programming skills integrated into the modules, allowing learners to engage in numerous practice problems.
The long-term vision of AI, including Edge operations, is explained in the program along with the principles required for implementing Edge AI. Learners will be able to distinguish and segment cloud and edge-based operations appropriately for real-world problems. Various exercise problems with relevant software and hardware architecture support learning Edge AI with suitable metrics.
Overall, learners will embark on an exciting journey of understanding and applying AI algorithms, processing these algorithms for edge applications, and implementing sample Edge AI solutions. The program also introduces Edge AI products available in the market, enabling learners to map their AI skills to suitable upcoming career options.
Program Objectives
AI & IoT
Understand the relationship between AI, Internet of Things (IoT) and Edge Computing.
AI Principles
Understand the fundamentals and principles of AI, Machine learning and Edge Computing.
ML Algorithms
Acquire knowledge on the fundamentals of machine learning algorithms.
ML Applications
Explore the potential applications of machine learning in various sectors.
Unsupervised Learning
Understand the fundamentals of unsupervised machine learning and its various types.
Reinforcement Learning
Explain the fundamentals of reinforcement learning algorithms and their types.
Neural Networks
Comprehend the operational principles of neural networks.
Weka Tool
Acquire knowledge about the functionalities of the Weka tool.
Gradient Issues
Address the issues of vanishing and unstable gradients in deep learning neural networks.
CNN & RNN
Introduce the fundamentals and applications of convolutional neural networks and recurrent neural networks.
AIoT Architecture
Learn the layered architecture of IoT with Artificial Intelligence.
Edge Computing
Understand the high computation machine (HCM) services at the edge and in the cloud.
Key Highlights
The step-by step explanation of AI principles offers a modular and reinforced learning of mathematics and science fundamentals of AI.
The edge computing principles motivations in the program enable the learner to investigate adaptive solutions of AI.
The edge computing demos provide an experiential learning for the learner to design new AI solutions.
Instructor
Dr. B Venkatalakshmi
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.