AI Horizons:
Scaling Scientific ML

for Educators and Researchers

Unlock the Power of 'Machine Learning in Academia and Research', The AI Empowered specialization is your gateway to a future where educators, faculty members, and researchers harness the potential of machine learning. Designed to demystify ML complexities, this program empowers you to lead with confidence in the era of artificial intelligence. Whether you aspire to teach cutting-edge courses or pioneer groundbreaking research, our dual-focused curriculum ensures that you master ML as both a subject of instruction and a potent research tool. Join us on this journey of academic excellence and research innovation.

Format

Online 
Course

Duration

6 weeks

Mode of Delivery

Live Lectures

Level

Intermediate

Start Date and Time

17th Jan 2024, after 6 PM on weekdays

Price

₹ 50,000 + 18% GST  
₹ 25,000 + 18% GST  

* Terms and Conditions apply

Target Group

The "AI Empowered" program caters to academic innovators, including educators, faculty members, and researchers who are committed to integrating machine learning (ML) into their teaching and research efforts.

Target Group

This program is designed for those in academia who want to master ML as both a subject of instruction and a powerful tool for research. It demystifies ML complexities for students while enabling research advancements.

Target Group

Academic professionals can use this program to teach cutting-edge courses and demystify ML for their students, fostering educational excellence.

Target Group

The program also empowers participants to harness ML's potential for pioneering research that advances the frontiers of science and technology.

Target Group

By providing a solid foundation in ML, this specialization equips academic professionals to lead with confidence in the era of artificial intelligence, whether in teaching or research.

 Eligibility Criteria

Faculty members actively engaged in teaching or research at accredited institutions.

Research scholars who are currently pursuing rigorous academic work and seek to incorporate machine learning into their studies.

Professionals who hold a minimum of 2 years of working experience in relevant fields and are looking to deepen their understanding of machine learning to enhance their professional capabilities


What is included ?

Interactive Lessons

Engage with interactive lessons that incorporate multimedia elements like videos, quizzes, and exercises to enhance your understanding and retention of the material.

Capstone Projects

The program culminates in capstone projects that challenge participants to create teaching modules or research projects. This not only fosters peer collaboration but also yields tangible outputs that can be directly integrated into their professional roles.

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Hands-on Labs

Each course within the specialization includes comprehensive lab sessions. These labs allow participants to apply theoretical knowledge to real-world scenarios, thereby solidifying their understanding and honing their practical skills

Assessments

Regular assessments and quizzes evaluate your understanding of the course material, track your progress, and provide valuable feedback on your learning journey.

Course Overview

Bridge Course: Tools and Libraries for Machine Learning

Foundational bridge course introduces essential ML tools, libraries, and software for development and deployment.

Introduction to Machine Learning for Educators and Researchers:

this course will cover the fundamental concepts and algorithms. It will provide a comprehensive overview tailored to educators and researchers, setting the groundwork for more advanced studies.

Scientific Machine Learning for Advanced Research

This course emphasizes ML integration with scientific inquiry, covering Physics-Informed Neural Networks (PINNs) and ML for solving Partial Differential Equations (PDEs) to empower researchers in cutting-edge scientific exploration.

Machine Learning at Scale for Computational Research

This course addresses the challenges of scaling ML applications for large

datasets and computational research. Participants will learn about high-performance computing techniques and frameworks like OpenMP, MPI,

CUDA, DASK, RAY, and parallel ML libraries

Tech Stack

Throughout the course, you will gain practical experience and proficiency in using these tools .

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