SUBHANKAR MISHRA

ଶୁଭଙ୍କର ମିଶ୍ର

Reader-F, School of Computer Sciences, NISER
ପାଠକ-ଏଫ, ସଂଗଣକ ବିଜ୍ଞାନ ବିଦ୍ୟାଳୟ, ନାଇଜର



CS460/660 - Machine Learning 2024 (2023-24 Even Semester)

Teaching Assistants

Syllabus

Expected skills (No mandatory prerequisite)

Grading scheme

Grading will be absolute.

Lectures

Week Topic
Week 1 Course Logistics, Introduction, ML Types, Learning, Inductive Bias
Tutorial 1 : Remote Devlopment on SSH Servers
Week 2 Linear SVM, CS Katha Barta Talk 1
Week 3 Linear Regression, Feature Engineering
Tutorial 2 : Git & GitHub: An Invitation to Version Control
Week 4 kNN, Decision Trees, CS Katha Barta Talk 2
Week 5 (Jan 30 - Feb 2) Project Proposal Presentations, CS Katha Barta Talk 3
Week 6 Decision Trees (Gini Impurity)
SVM MIT Youtube and Andrew Ng Note
Week 7 Soft margin SVM, Hyperparameter Tuning,
Model Assessment, Imbalanced Dataset
Week 8 (Feb 19 - Feb 23) Midterm Exam
Week 9 (Feb 26 - Mar 1) Midsemester Break
Week 10,11 (Mar 4 - Mar 15) Project Midterm Presentations
Week 12 Unsupervised Learning - Clustering, Bias/Variance,
Regularization, PCA/tSNE
Week 13 Ensemble Learning (Bagging, Boosting), Logistic Regression
Week 14 Linear Regression, Gradient Descent, Implementation
(Apr 05) Poster Presentations (Assignment) Library 3rd Floor
Week 15,16 (Apr 08 - Apr 19) Project Final Presentations

Assignment


Liquid Time-Constant Networks
Liquid Time-Constant Networks Jyotirmaya Shivottam
L-BFGS Optimization
L-BFGS Optimization Abhishek Singh
Segmentation -Threshold technique
Segmentation - Threshold technique Kartika Sahu
Softmax Regression
Softmax Regression Anuleho Biswas
Random Forests
Random Forests
Nissy Milcia William
Regression Metrics
Regression Metrics
Ipsita Rout
Attention Is All You Need
Attention Is All You Need Pritipriya Dasbehera
TRANSFORMERS
TRANSFORMERS Saptarshi Datta
Anomaly Detection
Anomaly Detection Samir Dileep
Demystifying the High Dimension: t-SNE
Demystifying the High Dimension: t-SNE Shourya
Market Basket Analysis: The Apriori Algorithm
Market Basket Analysis: The Apriori Algorithm Vanshaj
Hierarchical Density-Based Clustering: HDBSCAN
HDBSCAN Prayag R. Sahu
Suppl. Presentation
Uniform Manifold Approximation and Projection (UMAP)
UMAP Srinivas
Suppl. Presentation
Long Short Term Memory
Long Short Term Memory Aviral Verma
Autograd
Autograd
Rishav Das
Radial Basis Functions (RBF)
Radial Basis Functions (RBF)
Venkatesh Jha
CRNN
CRNN
Rabmit Das
GAN
Generative Adversarial Network
Rahul Madhav M
Sannu_Explainability
Explainability for AI in healthcare
Sannu
Sandipan_Bayesian_Inference
Bayesian Inference
Sandipan Samanta
XGBoost
XGBoost
A Rameswar Patro
GAN
Convex Optimization
Anshuman Panda
Aaditya_VICReg
VICReg
Aaditya Vicram Saraf
Swastik_regularization
Regularization in ML
Swastik Dewan
Tasneem_Autoencoder
Autoencoders
Tasneem Basra Khan
Normalization_sarthakpatel
Normalization
Sarthak Patel
Physics Informed Neural Networks
Physics Informed Neural Networks
Agney K Rajeev
AdaBoost
AdaBoost
Joel Joseph KB
Perceptron
Perceptron
M Sreerag

Project

A group of 2. Class project must be something new that you did in this semester. Two types of projects are allowed: Deliverables for reports will be in LaTeX generated pdf. Format should follow NeurIPS template with a plagiarism report. Codes and dataset must be shared over github/gitlab private repo.
  1. [05] (Max. 2 slides) Project Proposal - 5 mins
  2. [10] (Max. 5 slides) Project Midway - 10 mins + 5 mins Q&A, Midterm Report - Max Limit 4 pages
  3. [25] (Max. 10 slides) Final Project Presentation - 20 mins + 10 mins 10 mins, Endterm Report - Max Limit 8 pages
  4. [Extra 10] Paper Submission
    • [5] Average conference
    • OR
    • [10] Top conference - Deadlines
    Conference/Journal list: The final selection of the conference will be in coordination with the instructor.
Submission guidelines:
# Team Members Title Proposal Midterm Final
0 Jyotirmaya Shivottam Applying Mamba to GNNs Slides Slides Slides
--- Report Report
1 Pritipriya Dasbehera Prediction of Quantum Dynamics using Experimental Measurements Slide Slide Slide
Abhishek Singh --- Report Report
2 Swastik Dewan Harnessing ML for Atmospheric Retrieval of Exoplanets Slide Slide Slide
Tasneem Basra Khan --- Report Report
3 C L Srinivas BeeML Slide Slide Slide
M Sreerag --- Report Report
4 Vanshaj Vidyan Implementing EQTransformer and creating a simpler model for earthquake detection in Indian subcontinent Slide Slide Slide
Saptarshi Datta --- Report Report
5 Rabmit Das Machine Learning Approach for Metabolite Profiling in Complex Mixtures via NMR Data Slide Slide Slide
Rahul Madhav M --- Report Report
6 Joel Joseph K B Predicting Possible Oligomerization States of Protein Sequences Slide Slide Slide
Agney K Rajeev --- Report Report
7 Samir Dileep Analyzing Strategies for Voronoi Area Game Slide Slide Slide
Sandipan Samanta --- Report Report
8 Subhrajyoti Mishra Smart Water Management Slide Slide Slide
Anuleho Biswas --- Report Report
10 Rishav Das Instance Segmentation of Dark Matter Halos Slide Slide Slide
Ipsita Rout --- Report Report
11 A Rameswar Patro Improving ELSA and adding a theoretical framework Slide Slide Slide
Aaditya Vicram Saraf --- Report Report
12 Aviral Verma Machine Learning Based Digital Holographic Microscopy Slide Slide Slide
--- Report Report
13 Anshuman Panda Detection of Lineas of Europa Slide Slide Slide
Pradeep Kumar Baisakh --- Report Report
14 Prayag Ranjan Sahu De-noising of Fluorescence Microscopy Images using ML Methods Slide Slide Slide
Venkatesh Jha --- Report Report
15 Kartika Sahu Organ specific Cancer Diaagnosis: A Deep Learning Approach Slide Slide Slide
Sannu Kumar Nayak --- Report Report
16 Shourya Into the Gut microverse Slide Slide Slide
Nissy Milcia William --- Report Report
17 Sarthak Patel Use of Neural Networks in calculating the abundance of different species in exoplanet atmospheres Slide Slide Slide
--- Report Report

Books

Some recommended books on Machine learning.

Academic Integrity

Any plagiarism, copying, allowing copying, unpermitted aid will lead to 'zero' in the assignment/exam/project.

Past Courses