ML_TA_IIITB_2022

This repository contains material relevant to the AI511 Machine Learning Course

Course Instructors

• Prof. Dinesh Babu Jayagopi
• Prof. Neelam Sinha

Teaching Assistants

• Abhigna K L
• Adrij Sharma
• Aniruddh KB
• Debmalya Sen
• Harshita Soni
• Nikhil Agarwal
• Preyas Garg
• Vaibhavi Lokegaonkar
• Venkat S Bitra
• Vijay Jaisankar

Announcements

• All official announcements will be made on LMS.
• Students are encouraged to use Slack as a forum for discussions.

Quick Links

• 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
• Practice questions pertaining to various topics
• (Rather lengthy) project for practice and various approaches for this task.

Sessions' Files

• 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