Program Description
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 course 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 course 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 course 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 course also introduces Edge AI products available in the market, enabling learners to map their AI skills to suitable upcoming career options.
Course Objectives
At the end of this course learners will be able to,
- Understand the relationship between AI, Internet of Things (IoT) and Edge Computing.
- Understand the fundamentals and principles of AI, Machine learning and Edge Computing.
- Acquire knowledge on the fundamentals of machine learning algorithms.
- Explore the potential applications of machine learning in various sectors.
- Understand the fundamentals of unsupervised machine learning and its various types.
- Explain the fundamentals of reinforcement learning algorithms and their types.
- Comprehend the operational principles of neural networks.
- Acquire knowledge about the functionalities of the Weka tool
- Address the issues of vanishing and unstable gradients in deep learning neural networks.
- Introduce the fundamentals and applications of convolutional neural networks and recurrent neural networks.
- Learn the layered architecture of IoT with Artificial Intelligence.
- 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 course 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
Course enrollment data
Learning Format
Online
Duration
6 units
Certified by
IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras
and
L&T EduTech
Program Fee
Rs. 1900/- Inclusive of Tax
Downloads
Program Description
Education Qualification
Students pursuing Diploma / UG / PG Programs in Electrical & Electronics Engineering / Electronics and Communication Engineering
Suggested Prerequisites
Basic knowledge on Electronics Engineering
Teaching Hours
17
Lead Faculty
Dr. B Venkatalakshmi
Subject Matter Expert L&T EduTech.
A highly qualified academician and researcher with a Ph.D in Multisource Network Coding for MANETs and an ME in Optical Communication from College of Engineering Guindy, Anna University. Her research interests include pervasive computing, network coding, RFID, digital signal processing, information theory, mobile ad-hoc networks, industrial IoT, AI and edge computing and 5G. Dr Venkatalakshmi has a wide range of research skills, including Matlab, GIoMoSim, Qualnet, ADS, RFID API integration, Python, Weka and Power BI. With over 29 years of teaching and research experience, having served as a lecturer, professor, head of research and development department and vice principal at various educational institutions in Chennai, she has made significant contributions to academic planning and development, research planning and development and industry interactions and development. She has organised and developed the ME Mobile and Pervasive Computing syllabus and gained expertise in the domain of RFID and wireless sensor networks, training human resources in these areas. She has also established new research labs and published many research works in national and international journals.
