CS/CNS/EE 156a: Learning Systems (Fall 2023)

A repository of my solutions to the homework assignments, bonus exercise, and final exam for the Fall 2023 installment of CS/CNS/EE 156a: Learning Systems taught by Professor Yaser Abu-Mostafa at the California Institute of Technology.

Pre-requisites

Requires Python 3.10 and the following packages:

• CVXOPT
• Matplotlib
• NumPy
• pandas
• requests
• scikit-learn
• SciPy

Repository Structure

• The cs156a.py file in the main directory acts as a Python module with helper functions that are used throughout the homework assignments and final exam.
• The hw directory contains subdirectories for each homework assignment. Within the subdirectories, the hw*.docx and hw*.pdf files are the writeups, the hw*.py and hw*.ipynb files are functionally equivalent Python scripts and Jupyter Notebooks for the entire assignment, and the p**_**.py files are Python scripts for individual problems.
• Similarly, the final directory contains the writeup in final.docx and final.pdf, a Python script and Jupyter Notebook for the entire exam in final.py and final.ipynb, and Python scripts for individual problems in the p**_**.py files.
• The bonus directory contains the writeup in bonus.docx and bonus.pdf, completed bonus exercise Jupyter Notebook templates in Bonus_Part_1.ipynb and Bonus_Part_2.ipynb, and model data and figures generated during training in the various subdirectories, *.npy, and *.png files.

Note that the hw*.py and hw*.ipynb files, final.py, and final.ipynb import cs156a.py, while the p**_**.py files are standalone Python scripts that only depend on the Python standard library and the pre-requisite packages listed above.