DSCI 552 - Machine Learning for Data Science

Welcome to the comprehensive DSCI 552 - Machine Learning for Data Science repository! This repository serves as a centralized hub housing all coursework materials, including assignments, projects, and midterm solutions.

Throughout this course, we delve into the intricate world of machine learning techniques tailored specifically for data science applications. From foundational principles to advanced methodologies, this repository encapsulates a wealth of resources designed to enhance your understanding and proficiency in leveraging machine learning for robust data analysis.

Contained within this repository are meticulously organized folders, each representing distinct modules or components covered during the course. You'll find a treasure trove of assignments, meticulously crafted projects, and comprehensive solutions to midterms, providing you with invaluable insights and hands-on experience in applying machine learning algorithms to real-world datasets.

Caution

Please note that this repository serves as a reference guide and should be utilized as a tool for learning and comprehension. It's paramount to refrain from engaging in any activities associated with plagiarism. Embrace the wealth of knowledge herein to enhance your understanding and augment your skill set in the field of machine learning.

Warning

Before diving in, take a moment to peruse the license and disclaimer. Understanding the terms laid out will ensure responsible utilization of the resources within this repository, promoting ethical learning practices.

Feel free to navigate through the folders, explore the assignments, delve into the projects, and utilize the solutions provided as learning aids. Whether you're a novice eager to grasp the fundamentals or an experienced practitioner seeking to refine your skills, this repository aims to be your companion in mastering the intricate intersection of machine learning and data science.

We encourage you to engage actively, experiment with the materials provided, and embark on an enriching journey into the realm of Machine Learning for Data Science.

Happy learning!

Table of contents

Assignment	Topic Covered
Assignment 1	Classification, VC Dimension, Error Function Analysis
Assignment 2	Exploratory Data Analysis and K-Nearest Neighbors Classification
Assignment 3	Bias & Variance, EM, Bayesian Theory
Assignment 4	Decision Trees and Naive Bayes
Assignment 5	Image Classification on MNIST using simple NN, CNN
Assignment 6	Image Classification on CIFAR-10 dataset using CNNs
---	---
Midterm	Midterm 1
Project	Landmarks & Category Classification

Authors

1. Kayvan Shah | MS in Applied Data Science | USC

LICENSE

This repository is licensed under the BSD 5-Clause License. See the LICENSE file for details.

Disclaimer

_{The content and code provided in this repository are for educational and demonstrative purposes only. The project may contain experimental features, and the code might not be optimized for production environments. The authors and contributors are not liable for any misuse, damages, or risks associated with the use of this code. Users are advised to review, test, and modify the code to suit their specific use cases and requirements. By using any part of this project, you agree to these terms.}