Machine
Learning
Club.
Innovate beyond boundaries.

The stuff we do

Welcome to the Machine Learning Club of NIT Silchar, where we foster a vibrant community of students passionate about artificial intelligence and machine learning. If you're a student at our college with an interest in these cutting-edge fields, we invite you to join our dynamic club through our engaging classes and workshops. Our club is dedicated to educating students about machine learning, providing a platform for insightful discussions on AI trends, and offering valuable resources and guidance for hands-on learning and experimentation.

At the Machine Learning Club, we go beyond the classroom, organizing diverse events like hackathons, workshops, talks, and weekly classes that are open to all ML enthusiasts. Whether you're a beginner or an experienced practitioner, our club creates an inclusive environment for everyone to explore, learn, and collaborate. Join us to build a strong network, stay updated on the latest developments, and be a part of a community that is passionate about both research and development in the exciting realm of artificial intelligence.

Weekly ML Classes

Weekly classes to teach students eveything from basics to advanced

Speaker Sessions

Regular speaker sessions with relevant people working in the field

Hackathons

Our very own ML-based hackathon named 'Neurathon'

Paper Reading Sessions

Regular paper-reading sessions

Workshops

Hands-on community workshops to polish your practical skills

Projects

Development of innovative projects solving real-world issues

Project Showcase

Cough it
Research Paper
ConvNet model that classifies COVID-19 from cough sounds, achieving 87.07 AUC score.
Grad-CAM for the skin-mnist dataset for skin lesion diagnosis
Project
ConvNet model that detects skin lesions, explains predictions with Grad-CAM
Segmented sequence modeling in Indian classical music
Project
VAE detects landmarks in Indian Classical Music, published in IEEE 2022.
Textual Entailment as an Evaluation Metric for Abstractive Text Summarization
Research Paper
NLP that model summarizes text using Abstract Summarization and Textual Entailment.
Emotion Detection in Images of Faces
Project
Keras model that Classifies happy/sad faces using CNN.
Autonomous driving application Car detection
Project
Keras model that detects objects in images using YOLO and CNN.
RoadMent
Project
Segments roads from satellite images; won 2nd in Anveshan 2022.
Clean/Dirty Road Classifier
Project
Identifies clean/dirty roads using Transfer Learning; 97.1% training accuracy.
DiagnoAI
Project
DiagnoAI detects diseases from text using fine-tuned BERT for 24 diseases.
CalmSpace
Project
Analyzes recorded emotions, predicts sentiment with RNN, visualizes through graphs.
Flood Segmentation
Project
Flood segmentation model identifies flooded areas from aerial images using deep learning.
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