This repository contains material relevant to the AI511 Machine Learning Course
- Abhigna K L
- Adrij Sharma
- Aniruddh KB
- Debmalya Sen
- Harshita Soni
- Nikhil Agarwal
- Preyas Garg
- Vaibhavi Lokegaonkar
- Venkat S Bitra
- Vijay Jaisankar
- All official announcements will be made on LMS.
- Students are encouraged to use Slack as a forum for discussions.
- Resources will be added here
- Feel free to make a PR if you wish to share good resources or contribute to the repo in any other way.
If you have a proposal for the repo or would like to report a bug, please raise an issue.- Please read the contributing guidelines before creating an issue or a PR.
- Link for End-to-end deployment video
- Link for Convex optimisation: task 1
- Link for Neural networks: task 2
- Introduction to machine learning, pandas, numpy, and linear regression
- Preprocessing and data wrangling
- Gaussians, logistic regression, and naive bayes
- Kmeans clustering and principal component analysis
- Regularised regression and cross validation
- Decision trees and random forests
- Xgboost and end-to-end model deployment
- Convex optimisation and KKT conditions
- Support vector machines
- Introduction to neural networks and backpropagation