Introduction
Artificial
Intelligence (AI) has captivated our imaginations and been a focus of research
since a group of computer scientists coined the term at the Dartmouth
Conference in 1956. Over the decades, AI has been seen as a revolutionary force
capable of transforming our future. AI systems can sense, reason, act, and
adapt, enabling them to perform tasks that typically require human
intelligence. The rapid advancements in AI, Machine Learning (ML), and Deep
Learning (DL) are now evident in many aspects of our daily lives, from
personalized recommendations on retail sites to automated photo tagging on
social media.
Key Concepts
- Artificial Intelligence
(AI): AI
refers to the simulation of human intelligence in machines. These systems
are designed to perform tasks that typically require human cognition, such
as understanding natural language, recognizing patterns, and making
decisions. AI encompasses a wide range of technologies, including expert
systems, neural networks, and robotics.
- Machine Learning (ML): ML is a subset of AI
focused on developing algorithms that enable machines to learn from data.
These algorithms can improve their performance as they are exposed to more
data over time. ML can be broadly categorized into:
- Supervised Learning: Algorithms learn from
labeled data, making predictions or decisions based on previously seen
examples.
- Unsupervised Learning: Algorithms identify
patterns and relationships in unlabeled data without predefined labels or
outcomes.
- Reinforcement Learning: Algorithms learn by
interacting with their environment, receiving rewards or penalties based
on their actions, and optimizing their behavior to maximize rewards.
- Deep Learning (DL): DL is a specialized area of
ML that uses neural networks with multiple layers (deep neural networks)
to process and analyze vast amounts of data. DL has revolutionized fields
such as computer vision, natural language processing (NLP), and speech
recognition, enabling machines to perform tasks with unprecedented
accuracy and efficiency.
Course Overview
This
comprehensive course introduces the fundamental concepts of AI, ML, and DL,
with a particular emphasis on their applications in geospatial data processing.
Participants will explore the theoretical foundations, practical methodologies,
and real-world applications of these technologies. The course will cover:
- Introduction to AI, ML, and
DL: An
overview of the history, evolution, and current trends in AI, ML, and DL,
highlighting their significance and impact on various industries.
- Machine Learning Methods: Detailed exploration of
supervised, unsupervised, and reinforcement learning techniques, including
their theoretical underpinnings, algorithms, and applications.
- Deep Learning Concepts: In-depth study of advanced
DL techniques, such as convolutional neural networks (CNN), recurrent
neural networks (RNN), region-based convolutional neural networks (R-CNN),
Faster R-CNN, single shot detector (SSD), You Only Look Once (YOLO), and
their applications in spaceborne lidar systems.
- Machine Learning with Google
Earth Engine:
Practical applications of ML using Google Earth Engine, a powerful tool
for analyzing and visualizing geospatial data.
- Python for ML/DL Models: Hands-on experience with
Python programming for developing and deploying ML and DL models,
including data preprocessing, model training, evaluation, and deployment.
Course Details
- Schedule: August 19-24, 2024
- Eligibility: This course is designed for
professionals, students, and researchers in civil engineering, computer
science, data analytics, geoinformatics, and geomatics who are eager to
learn about AI, ML, and DL and apply these technologies to geospatial data
analysis.
- Sponsor: The program is sponsored by
the Indian Space Research Organisation (ISRO), Department of Space,
Government of India.
- Fee: There is no fee for
attending this course.
- Materials: Participants will have
access to a wealth of learning resources, including lecture slides, recorded
video lectures, open-source software, and demonstration handouts, all
available through the e-class platform. Video lectures will also be
uploaded to the IIRS e-class for easy
access.
Registration
- Nodal Centre Registration: Participants can register
through their respective nodal centres. The registration must be approved
by the coordinator of the nodal centre. Participants can register and
check their application status . If the application
is pending approval, participants should contact the coordinator of their
nodal centre.
- Individual Registration: Participants opting for individual registration will be automatically approved. They will receive their login credentials for the ISRO Learning Management System (LMS) at ISRO LMS.
Certification
Participants
must attend at least 70% of the sessions to receive a "Course
Participation Certificate." Certificates will be available for download
through the ISRO LMS. This certification will serve as a testament to the
participants' commitment to learning and their understanding of AI, ML, and DL
concepts and applications.
Technical Requirements
To
participate in the course effectively, attendees will need:
- For Classroom Participation: A desktop or laptop
computer equipped with a web camera, microphone, and output speakers, as
well as a large display screen, projector, or TV for group viewing.
- Internet Connectivity: Reliable internet
connectivity is essential for accessing the e-class platform, streaming
video lectures, and participating in online discussions.
For more
details and updates, visit the IIRS Edusat News.
Course Impact and Opportunities
This course offers an invaluable opportunity for professionals, students,
and researchers to delve into the transformative world of AI, ML, and DL. By
understanding the core principles and gaining hands-on experience with these
technologies, participants will be well-equipped to leverage AI and ML for
solving complex problems, enhancing productivity, and driving innovation in
their respective fields. The specific focus on geospatial data processing will
provide participants with the skills needed to harness the power of AI for
applications such as remote sensing, geographic information systems (GIS),
environmental monitoring, and urban planning.
Furthermore, the sponsorship by ISRO underscores the course's credibility
and the importance of AI and ML in advancing space research and geospatial
technologies in India. Participants will also benefit from the networking
opportunities provided by the course, connecting with peers, experts, and
leaders in the field of AI and geospatial analysis.
