This repository contains a collection of assignments for the Statistical Methods in AI Spring 2023 course prepared during my tenure as a Teaching Assistant along with other TAs. Each assignment is organized into its own directory and includes a set of questions, code, and data related to various topics in AI and machine learning.

• K Nearest Neighbour
  • rollnumber_A1_Q1.ipynb
• Decision Trees
  • rollnumber_A1_Q2.ipynb
• Linear Regression
  • rollnumber_A1_Q3.ipynb

• Clustering
  • Q1.ipynb
• Principal Component Analysis
  • Q2.ipynb
• Multinomial Naive Bayes
  • Q3.ipynb
• Gaussian Naive Bayes
  • Q4.ipynb

• CNNs
  • Let's Dive into CNNs.ipynb
• Gaussian Mixture Models
  • GMM.ipynb
• Multi-Layer Perceptron
  • Knowing MLPs.ipynb
• Support Vector Machine
  • SVM.ipynb

To make the best use of this repository, you can clone it to your local machine and access the assignments you need. Each assignment directory contains the necessary files to work on the questions and complete the tasks.

If you find any issues or have suggestions for improvements, please feel free to create a pull request or open an issue. We welcome contributions and feedback from fellow learners and contributors.

Happy learning!