ଶୁଭଙ୍କର ମିଶ୍ର

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

CS460/660 - Machine Learning 2023 (2022-23 Even Semester)


Expected skills (No mandatory prerequisite)

Grading scheme

Grading will be absolute.


Week Lec # Topic Slide
Week 1 1 Course Logistics and Introduction
2 Generalization Group1
3 Why and when Machine Learning,
4 Inductive Learning, Structured Data
Week 2 5 Decision Trees Group2
6 Inductive Bias
7 Expected Loss, Inductive vs Transductive learning Group3
8 General approach for Machine Learning
Week 3 9 Types of Machine Learning (Based on Input and Output) Group4
10 Supervised Learning - Linear SVM Group5
11 Supervised Learning - Linear SVM (Continued)
12 Linear Regression Group6
Week 4 13 Linear Regression (Continued)
14 Gradient Descent Group7
15 Gradient Descent (Continued)
16 Logistic Regression Group8
Week 5 (Jan 30 - Feb 3) Project Proposal Presentations
Week 6 - Quiz - 1
17 Decision Trees - CART Group9
18 Decision Trees - CART - Continued
- Decision Trees - ID3 Group10
19 SVM - Hinge Loss Group11
Week 7 20 SVM - Kernel Functions CS229 Notes - Andrew Ng Group12
21 SVM - Kernel Functions - Continued
22 kNN Group13
- kNN Implementation
Week 8 23 Feature Engineering (One-hot encoding, bucketing, normalization, standardisation, missing features, imbalanced dataset) Group14
24 Model Performance Assessment Group15
25 Hyperparameter Tuning
Week 9 26 Unsupervised Learning - Clustering - kMeans Group16
27 Unsupervised Learning - Clustering - DBSCAN
28 Unsupervised Learning - How to find the number of clusters?
Week 10,11 (Mar 6 - Mar 17) Project Midterm Presentations
Week 12 29 Unsupervised Learning - Dimensionality reduction PCA Group17
30 Unsupervised Learning - Dimensionality reduction tSNE
31 Bias and Variance Group18
32 Regularization
Week 13 33 One-class classification and Multiclass classification Group19
34 Multilabel classification
35 Ensemble Learning - Bagging Group20
36 Ensemble Learning - Boosting
Week 14 37 XGBoost Group21
38 AdaBoost
39 Naive Bayes Classifier Group22
40 Cross Entropy Loss Group23
Week 15,16 (Apr 10 - Apr 21) Project Final Presentations



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. Updates:
  1. 25 Jan, 2023 - You need to run a plagiarism check for all the reports to submit and share the result as well.
  2. 02 Feb, 2023 - The codes and dataset will be shared over github (private repo is fine if the work is to be published.)
  1. [05] (Max. 2 slides) Project Proposal - 5 mins
  2. [10] (Max. 10 slides) Project Midway - 15 mins + 5 mins Q&A, Midterm Report - Max Limit 4 pages
  3. [15] (Max. 15 slides) Final Project Presentation - 20 mins + 10 mins 10 mins, Endterm Report - Max Limit 8 pages
  4. [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 Paper
1 Sagar Prakash Barad Estimation of Electronic Band Gap Energy Using Machine Learning Slide Slide Slide Slide
Sajag Kumar --- Report Report Report
2 Aritra Mukhopadhyay Improvement on the Quaternion-based models: extension to larger datasets and Batch Normalization Slide Slide Slide Slide
Adhilsha A --- Report Report Report
3 Aniket Nath Using Self-supervised pre-trained model fine-tuned with Active Learning algorithm to find Dual Active Galactic Nuclei in SDSS Dataset. Slide Slide Slide Slide
Diptarko Choudhury --- Report Report Report
4 Debashish Paik Analysis of Economy using nighttime light data Slide Slide Slide Slide
Rohan Naskar --- Report Report Report
5 Dev Shrivastva Detection of Retinal Diseases Slide Slide Slide Slide
Shubhanshu Prasad --- Report Report Report
6 Priyanshu Parida Differentiation of Neutron and Gamma Response for EJ-301 Detector at low energy Slide Slide Slide Slide
Nehal Khosla --- Report Report Report
7 Arshia Anjum Infer accreted mass fractions of central galaxies Slide Slide Slide Slide
Sibabrata Biswal --- Report Report Report
8 Dibya Bharati Pradhan Exoplanetary surface composition prediction using ML Slide Slide Slide Slide
Oommen P Jose --- Report Report Report
9 Ayush Singhal Analysing Exoplanetary Atmospheres using ML Slide Slide Slide Slide
Gaurav Shukla --- Report Report Report
10 Akankshya Nayak Expressivity of geometric GNN Slide Slide Slide Slide
Soumya Dasgupta --- Report Report Report
11 Chandan Kumar Sahu Studying the interior evolution of rocky exoplanets using machine learning Slide Slide Slide Slide
Maitrey Sharma --- Report Report Report
12 Anna Binoy Application of ML in Predicting Gaseous Properties of Earth's Atmosphere Slide Slide Slide Slide
Sumegha M.T. --- Report Report Report
13 Rahul Vishwakarma Indoor localization using WiFi RSSI fingerprinting Slide Slide Slide Slide
Jyotish Kumar J. --- Report Report Report
14 Arpan Maity Gradient Boosted Decision Trees and their application in improvising identification of decay of Higgs Bosons into pair of electrons Slide Slide Slide Slide
Krishna Kant Parida --- Report Report Report
15 Anuleho Using Federated Transfer Learning to correlate sleeping health to digital wellbeing Slide Slide Slide Slide
Abhijit --- Report Report Report
16 Ratul Das Phase Transition Detection without Order Parameters Slide Slide Slide Slide
Mihir Chandra --- Report Report Report
17 Ritadip Bharati ML in Fashion Slide Slide Slide Slide
Sudip Kumar Kar --- Report Report Report
18 Krishanu Kanta Analysing Seismic data of Palghar using ML. Slide Slide Slide Slide
Sagnik Rout --- Report Report Report
19 Sunaina Predicting breeding sites of Desert Locust through environmental parameters Slide Slide Slide Slide
Bibhu --- Report Report Report
20 Fida Salim Retrieving Pressure-Temperature and Water Vapour Profiles in Earth’s Atmosphere from INSAT 3DR data using Machine Learning Slide Slide Slide Slide
Soumik Bhattacharyya --- Report Report Report
21 Deependra Singh Retinal Fundus Multi-Disease Image Classification Slide Slide Slide Slide
Saksham Agarwal --- Report Report Report
22 Kaling Vikram Singh Prediction of Lattice Structure of perovskites from physical properties using ML Slide Slide Slide Slide
Sunil S Raja --- Report Report Report
22 Summit Bikram Nayak Quantitative Analysis of Lipid Droplets using Image processing Slide Slide Slide Slide
Ankur Abhijeet --- Report Report Report


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