SUBHANKAR MISHRA

ଶୁଭଙ୍କର ମିଶ୍ର

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

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Teaching Assistants

• (Lead TA) Rucha Bhalchandra Joshi (PhD, CS)
• Adhilsa A (MSc, SPS)
• Aniket Nath (MSc, SPS)
• Aritra Mukhopadhyay (MSc, SPS)
• Deependra Singh (MSc, SPS)
• Diptarko Choudhury (MSc, SPS)
• Rahul Vishwakarma (MSc, SMS)
• Sagar Prakash Barad (MSc, SPS)

Syllabus

• Learning, Inductive Bias, Features, Labels, Basics of Statistics and Probability
• Supervised Learning - Some models
• Ensemble Methods: Bagging, Boosting. Learning Theory
• Unsupervised Learning - Some models
• Reinforcement Learning Basics (if time permits)

Expected skills (No mandatory prerequisite)

• Algorithms (CS301 or equivalent)
• Python
• Probability and Statistics

Grading scheme

Grading will be absolute.

• Project - 40 marks
• Assignment - 10 marks
• Midterm - 10 marks [may include programming]
• Endterm - 40 marks [may include programming]
• Total -100 marks

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

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Assignment

• [10] Poster Presentation
• Cover a topic/algorithm covered in class or related. Finalise after discussion with associated TA and instructor
• Sample codes as required should be shared publicly over github or otherwise
• Follow the github clone and pull request to update your slides, posters will be made public
• Upload both .pdf as well as .zip containing the source for your poster.

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Project

A group of 2. Class project must be something new that you did in this semester. Two types of projects are allowed:

• Category - 1: You pick/create an interesting (new) dataset, apply best suited one or more well known machine learning algorithms as baselines and extend these baselines in your creative and interesting way.
• Category - 2: A theoretical project should look at an open question, definite it concretely, look at the literature, design and develop creative and interesting attempts.

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
• Title, dataset, idea, relevant papers, teammate with work division, what to do by Midway, what baselines to implement, what are the expected results
2. [10] (Max. 5 slides) Project Midway - 10 mins + 5 mins Q&A, Midterm Report - Max Limit 4 pages
• Insightful analysis of 2-3 related papers.
• Experiments you have done along with results.
3. [25] (Max. 10 slides) Final Project Presentation - 20 mins + 10 mins 10 mins, Endterm Report - Max Limit 8 pages
• Review
• New and interesting attempts to solve the problem
• Plan for further work for submission
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:

• Clone GitHub
• index.html
• If this is the first time, create a new row in the table, add your members, project title and initialize the groups slides and reports.
• You will add/update the links associated with your group.
• Projects folder
• If this is the first time, create a new folder under projects folder
• Add/update your files in that folder only. Do not make any changes other than your group/individual folder
• Create a pull request after the changes are done

#	Team Members	Title	Proposal	Midterm	Final
0	Jyotirmaya Shivottam	Applying Mamba to GNNs	Slides	Slides	Poster
			---	Report	Report
1	Pritipriya Dasbehera	Prediction of Quantum Dynamics using Experimental Measurements	Slide	Slide	Poster
	Abhishek Singh		---	Report	Report
2	Swastik Dewan	MLARE:ML-driven Atmospheric Retrieval of Exoplanets	Slide	Slide	Poster
	Tasneem Basra Khan		---	Report	Report
3	C L Srinivas	BeeML	Slide	Slide	Poster
	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	Poster
	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	Poster
	---		Report	Report
13	Anshuman Panda	Detection of Lineas of Europa	Slide	Slide	Poster
	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

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Books

Some recommended books on Machine learning.

• The Elements of Statistical Learning Link
• Mathematics for Machine Learning Link
• Introduction to Machine Learning with Python Link
• A course in Machine Learning Link

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Academic Integrity

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

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Past Courses

• 2023 CS460/660 - Machine Learning
• 2021 CS460/660 - Machine Learning
• 2020 CS460/660 - Machine Learning