About Event

Machine learning plays a crucial role in addressing complex computer vision challenges. In turn, many machine learning techniques have been specifically developed to enhance computer vision applications. Deep learning, in particular, has transformed computer vision by enabling algorithms to automatically learn hierarchical representations from raw image data. Prominent architectures like Convolutional Neural Networks (CNNs) have set new benchmarks in accuracy for tasks such as object recognition, image segmentation, and object detection. Despite these advancements, challenges remain, including the need for vast amounts of labeled data and enhancing model interpretability. Active research continues to address these issues while exploring new applications for deep learning in computer vision.
This STTP aims to provide participants with a comprehensive introduction to machine learning and deep learning concepts from a mathematically grounded perspective. Participants will explore fundamental and advanced supervised learning algorithms, including linear regression, decision trees, k-nearest neighbors, Bayesian learning, and neural networks, along with an introduction to deep learning and unsupervised clustering algorithms. Additionally, the STTP will focus on practical applications of machine learning techniques to tackle various computer vision problems. This STTP also includes hands-on problem solving with programming in Python and some tutorial sessions. Eminent resource persons will be handling the sessions.

Who Can Attend:

CSE AIDS ECE IT EEE Instrumentation Medical MCA Telecommunication BioMedical Aeronautical Aerospace School

Accommodation

Limited in-campus accommodation will be provided to the participants on request and payment basis (Rs.200/- per day)

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