Data Science and Artificial Intelligence I

Johann Mitloehner, 2023

• Introduction to Neural Networks
  • pre-reading Tariq Rashid, Make Your Own Neural Network, CreateSpace, 2016
  • Neural Net Introduction: One-layer Feed-Forward ||| nb
  • Examples: Single-Layer
• Extending the basic model
  • Two-layer Net and Backpropagation ||| nb
  • More Examples: NN with mushrooms and sentiment sentences
• Connectionist Machine Learning Framework: PyTorch
  • Adaptive models with PyTorch ||| nb
• Deep learning, word embeddings, and business applications
  • Embeddings, LSTM ||| nb
  • Comparison: Decision Tree
• Transformers and Large Language Models
  • Transformers, GPT, BERT ||| nb
  • Build your own Transformer ||| nb
  • Post-module assignment:
    • business application problem solved in connectionist machine learning
    • dataset e.g. from UCI repository
    • submit via Canvas

Your very own Jupyter notebook server is ready for you at the Canvas web page for this lecture. You can use the server via your web browser.

However, since we only use free Open Source software you can install everything on your own computer.

• If needed, get Python from python.org and click 'Add to Path' when you run the installer.
• Use pip on the command line to install additional packages as needed, e.g.
  pip install jupyter pandas matplotlib scikit-learn

More Examples:

• Dec 1, 2023: UCI Wine Dataset
• Dec 22, 2023: UCI Student Performance

Older videos:

• Neural Net Intro
• Jupyter Notebook
• Multi-layer and Backprop
• Simple RNN, LSTM, Decision Tree
• DT v2