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Machine Learning Course Website

This directory contains a comprehensive machine learning course website with multiple courses and topics.

Directory Structure

Main Courses

  • ml1/ - Introduction to Machine Learning (11 lectures: 01-07, 10, 12-14)

    • Basic ML concepts and foundations
    • Each lecture contains index.html with course content

  • ml2/ - Advanced Machine Learning (15 lectures: 01-15)

    • Advanced topics and techniques
    • Each lecture includes:
      • index.html (main lecture page)
      • markdown textbooks (ml2_weekXX.md)
      • Jupyter notebooks (homework and exercises)
      • assets/ folder with images and resources

Topic Libraries

  • mltopics/ - 10 standalone ML topics with textbook content
  • topics/ - Extensive topic library (32+ topics) including:
    • autoencoders, automl, backpropagation, bayesian_methods
    • boosting_bagging, cnn_architectures, cross_validation
    • decision_trees, dimensionality_reduction, GANs
    • gradient_descent, k_nearest_neighbors, learning_rate_schedulers
    • linear_regression, LSTM, neural_networks, regularization
    • recommendation_systems, search_algorithms, text_embeddings
    • time_series, transformers, t-SNE, UMAP, and more
    • Contains template files and generation prompts

Supporting Directories

  • python/ - Python programming lectures (4 lectures)
  • output/ - Generated PDF outputs organized by course
  • venv/ - Python virtual environment

Key Scripts

Content Generation

  • generate_pdfs.py - Converts markdown textbooks to PDFs
  • generate_ml2_homeworks.py - Generates ML2 homework assignments (117KB)
  • generate_ml2_notebooks.py - Creates ML2 Jupyter notebooks
  • update_ml2_pages.py - Updates ML2 lecture pages

Content Management

  • add_homework_buttons.py - Adds homework UI buttons to pages
  • course_generator_prompt.md - Template for course content generation

Web Files

  • index.html - Main website homepage
  • resource-library.html - Resource library page (326KB)
  • slideformat.txt - Slide formatting guidelines

Configuration

  • requirements.txt - Python dependencies
  • .gitignore - Git ignore rules
  • README.md - Project documentation

Workflow

  1. Write course content in markdown files
  2. Use generation scripts to create homework/exercises
  3. Run generate_pdfs.py to create PDF versions
  4. Update pages with update_ml2_pages.py
  5. Add interactive elements with add_homework_buttons.py