📘 Course Overview:
This repository contains my coursework (assignments, semester exams & project) for the Statistical Machine Learning course at IIIT Delhi in Winter 2024.
The course covers key topics in machine learning and pattern classification. Fundamentals and advanced theoretical and mathematical concepts related to classification techniques and learning paradigms are covered in this course.
📂 Folder Structure:
/Assignment 1: This folder includes assignment 1, it's official solution and my solutions are also included./Assignment 2: This folder includes assignment 2 codes, it was about applying QDA & PCA on the MNIST dataset & making handwritten digit recognition classifier model./Assignment 3: This folder includes assignment 3 codes, it was about learning a decision tree from the MNIST dataset and then use ensemble learning using bagging with decision trees to make a classifier model./Assignment 4: This folder includes assignment 4 codes, it was about implementing Boosting techniques. The first task was to apply AdaBoost.M1 algorithm on the MNIST dataset for binary classification of digits 0 and 1. The second task was to apply gradient boosting with absolute loss to a regression problem./Problem Sets: This folder includes all the problem sets given throughout the semester./Quizzes: Contains materials related to the quizzes./Midsem: Contains materials related to the midterm examination./Endsem: Contains materials related to the endterm examination./Project: Fraud Detection System for Fintech Banking Transactions
📌 Note: All algorithms & techniques are hard-code implemented from scratch without using any inbuilt python libraries.
📅 Semester: 4th Semester (2nd Year)
📚 Course Details:
Instructor's name - A V Subramanyam ([email protected])
Course Code - CSE342
📌 Important: Please make sure to follow the guidelines and policies outlined by the institution regarding the use of shared coursework materials. Use this repository responsibly and avoid any violations of academic integrity. Codes are provided for reference purposes only. It's recommended to understand the codes and implement them independently.