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SMMhomeworks - Advanced Solutions in Statistical Learning and Machine Learning

Description

This repository, "SMMhomeworks," contains comprehensive solutions and analyses for four key homework assignments in the realm of Statistical and Machine Learning. The repository is structured to include both the original PDFs of the homework assignments and Jupyter notebooks that provide detailed code solutions.

Topics Covered

Linear Algebra and Floating Point Arithmetic

  • Overview: This section delves into the foundational concepts of linear algebra, emphasizing their application in floating point arithmetic computations.

SVD and PCA for Machine Learning

  • Overview: Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are explored in this part, showcasing their significance and implementation in Machine Learning contexts.

Optimization via Gradient Descent and SGD

  • Overview: Focuses on the methodologies of Gradient Descent and Stochastic Gradient Descent, offering insights into optimization techniques in machine learning.

MLE and MAP

  • Overview: The final section covers Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP), highlighting their roles and applications in statistical inference and machine learning models.

Features

Each notebook not only presents the code solutions but also includes plotted results and discussions on possible theoretical content, providing a holistic understanding of the topics.