{"id":21192533,"url":"https://github.com/omerdahan1/machine_learning_course","last_synced_at":"2026-05-19T11:32:08.487Z","repository":{"id":246674309,"uuid":"821817097","full_name":"OmerDahan1/Machine_Learning_Course","owner":"OmerDahan1","description":"This repository contains my solutions and projects for a machine learning course. 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The course covers various topics and assignments, each of which is organized into separate folders.\n\n## Table of Contents\n  1. [HW1 - Linear Regression](hw1-Linear-Regression)\n  2. [HW2 - Decision Trees](hw2-decision-trees)\n  3. [HW3 - MAP Classifier](hw3-map-classifier)\n  4. [HW4 - Logistic Regression, Bayes, and EM](hw4-logistic-regression-bayes-and-em)\n  5. [HW5 - PAC Learning](hw5-pac-learning)\n  6. [HW6 - Clustering - k-means](hw6-clustering-k-means)\n  7. [How to Run the Code](how-to-run-the-code)\n\n## HW1 - Linear Regression\n### Files\nhw1.py\nhw1.ipynb\ndata.csv\n### Description\nThis folder contains my solutions and implementations for Homework 1, which focuses on Linear Regression. It includes the following components:\n  - Single Variable Linear Regression\n  - Gradient Descent\n  - Pseudoinverse\n  - Forward Feature Selection\n  - Multivariate Linear Regression\n\n## HW2 - Decision Trees\n### Files\nhw2.py\nhw2.ipynb\nagaricus-lepiota.csv\n### Description\nIn Homework 2, I worked on Decision Trees. This folder includes:\n  - Gini Impurity\n  - Entropy Value\n  - Goodness of Split\n  - Depth Pruning\n  - Chi Pruning\n\n## HW3 - MAP Classifier\n### Files\nhw3.py\nhw3.ipynb\ndata directory that contains csv files\n### Description\nHomework 3 is centered around the MAP Classifier. It comprises:\n  - Maximum Likelihood Estimation\n  - Normal Naive Bayes Classifier\n  - Full Bayes Classifier\n    \n## HW4 - Logistic Regression, Bayes, and EM\n### Files\nhw4.py\nhw4.ipynb\ndata.csv\ntraining_set.csv\ntest_set.csv\n### Description\nThe fourth assignment delves into Logistic Regression, Bayes, and EM. It encompasses:\n  - Pearson Correlation \u0026 Feature Selection\n  - Cross-Validation\n  - Normal Distribution PDF\n  - Expectation Maximization\n  - GMM (Gaussian Mixture Model)\n  - Naive Bayes\n  - Model Evaluation\n    \n## HW5 - PAC Learning\n### Files\nHW5.pdf\nEx5 PAC-Learnable.ipynb\n### Description\nAssignment 5 is a comprehensive exercise on PAC learning, VC dimension, kernels, and Lagrange multipliers. The assignment includes both theoretical questions and practical tasks:\n  - PAC Learning and VC Dimension\n  - Calculation of VC dimension and sample complexity.\n  - Polynomial sample complexity algorithm.\n  - Comparison of sample complexity bounds.\n  - VC Dimension of Decision Trees\n  - Analysis of VC dimension for x-node decision trees.\n  - Kernels and Mapping Functions\n  - Finding kernel functions and their corresponding mapping functions.\n  - Efficiency of kernel methods.\n  - Lagrange Multipliers\n  - Finding minimum and maximum points under constraints using Lagrange multipliers.\n  - Notebook Exercise\n  - Practical implementation and analysis based on the notebook exercises.\n    \n## HW6 - Clustering - k-means\n### Files\nhw6.py\nhw6.ipynb\n### Description\nThe sixth and final homework assignment focuses on Clustering with k-means. It includes:\n  - Implementing k-means and k-means++ algorithms.\n  - Running each algorithm 10 times.\n  - Visualizing the performance differences.\n    \n## How to Run the Code\n1. **Clone the repository:**\n   ```bash\n   git clone https://github.com/yourusername/machine-learning-course.git\n   ```\n2. **Open the appropriate Jupyter notebooks for each assignment:**\n    - Homework 1:\n    ```bash\n    jupyter notebook hw1.ipynb\n    ```\n    - Homework 2:\n    ```bash\n    jupyter notebook hw2.ipynb\n    ```\n    - Homework 3:\n    ```bash\n    jupyter notebook hw3.ipynb\n    ```\n    - Homework 4:\n    ```bash\n    jupyter notebook hw4.ipynb\n    ```\n    - Homework 5:\n    ```bash\n    jupyter notebook Ex5 PAC-Learnable.ipynb\n    ```\n    - Homework 6:\n    ```bash\n    jupyter notebook hw6.ipynb\n    ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomerdahan1%2Fmachine_learning_course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fomerdahan1%2Fmachine_learning_course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomerdahan1%2Fmachine_learning_course/lists"}