I am pursuing a Master of Science in Artificial
Intelligence from University of Michigan completing the
program between Aug, 2023 and May, 2025. This advanced
degree honed my expertise in key AI domains including
Deep Learning, Natural Language Processing, Computer
Vision, and Reinforcement Learning.
Concentration Area- Computer Vision
1. Natural Language Processing (NLP):
This course focused on the intricacies of enabling
machines to understand, interpret, and respond to human
languages. It covered various aspects of syntax,
semantics, and pragmatics in NLP, along with
applications in text analysis and voice recognition
systems.
2. Deep Learning (DL):
Deep Learning delved into advanced neural network
architectures and algorithms, emphasizing their
applications in image and speech recognition. The course
also explored the theoretical foundations and practical
implications of deep learning in various AI domains.
3. Pattern Recognition and Neural Networks:
This subject provided an in-depth understanding of the
techniques used in recognizing patterns and regularities
in data using neural networks. It covered both the
theoretical aspects and practical applications,
emphasizing their role in data analysis and predictive
modeling.
4. Computational Learning:
The course covered the principles and algorithms of
machine learning, focusing on the computational aspects.
It included topics like supervised and unsupervised
learning, reinforcement learning, and the design of
efficient learning systems.
5. Artificial Intelligence (AI):
This subject offered a comprehensive overview of AI, its
history, key concepts, and its impact on technology and
society. It also provided insights into various AI
approaches, including expert systems, search algorithms,
and knowledge representation.
6. Design Analysis and Algorithms:
This course focused on algorithmic design and analysis,
offering insights into the development of efficient
algorithms to solve complex computational problems. It
included topics like algorithmic strategies, complexity
analysis, and optimization techniques.
7. Software Engineering:
This subject emphasized the principles of software
development and engineering, covering the software
development lifecycle, methodologies, and best practices
in software design and maintenance.
8. Computer Graphics:
The course provided an understanding of the principles
and practices in computer graphics, including rendering
techniques, 3D modeling, and animation. It also explored
the use of graphics in simulation, gaming, and virtual
reality.
9. Information Visualization and Virtualization
(IVV):
This subject delved into the techniques and tools for
visualizing complex data and virtual environments. It
covered topics like data representation, interactive
visualization methods, and the creation of immersive
virtual experiences.
10. Robotic Vision:
Focusing on the intersection of robotics and computer
vision, this course explored the techniques used in
enabling robots to perceive, understand, and interact
with their environment through visual inputs. Topics
included image processing, object recognition, and 3D
vision.
