A collection tutorial materials for the Fall 2018 CSC411/CSC2515 (Introduction to Machine Learning) at the University of Toronto. The files are organized into subdirectories by the week they were presented. Most development is done with python 3.7.
Each week's subdirectory contains a mixture of .pdf, .py, and .ipynb files.
Additionally, there may be the file used to generate the .pdf which can be a .pptx, .key, or .tex.
Currently, the only requirements for running any .py or .ipynb is numpy.
Constructing a python 3.6+ environment is highly recommended.
This can be easily set up with conda - for example:
conda create --name py37 python=3.7
A conda cheat-sheet is located here.
The requirements can be installed with python's package manager, pip, or conda.
For example, inside your python environment with pip:
pip install numpy
Each week contains a README with guidance on how to present the material to the class.
.
├── README.md
└── week2
├── CSC411_Fall_2018_Tutorial1_lecture.ipynb
├── CSC411_Fall_2018_Tutorial1_worksheet.ipynb
├── README.md
├── linear_algebra_goodfellow.key
├── linear_algebra_goodfellow.pdf
└── supplementary_linear_algebra_srihari.pdf
1 directory, 7 files
All instructors and assistants for CSC411/CSC2515 Fall 2018 at The University of Toronto.