Syllabus

This is a course on scientific computing using Python. We'll cover aspects of the Python language as they are relevant to the material. The following schedule should be seen as a high-level guide to what we'll do in 8 lectures, but is not set in stone. Please check regularly, as lectures are added as we progress.

1. Python basics
   • pre-lecture: [ipynb], [colab]
   • post-lecture: [ipynb], [colab]
2. Object-oriented programming in Python
   • pre-lecture: [ipynb], [colab][Code for Exercise 1]
   • post-lecture: [ipynb], [colab]
3. Basics of NumPy
   • pre-lecture: [ipynb], [colab]
   • post-lecture: [ipynb], [colab]
4. Linear Algebra in Numpy
   • pre-lecture: [ipynb], [colab]
   • post-lecture: [ipynb], [colab]
5. Intro to Scipy
   • pre-lecture: [ipynb], [colab]
   • post-lecture: [ipynb], [colab]
6. Dataframes with Pandas
   • pre-lecture: [ipynb], [colab]
   • post-lecture: [ipynb], [colab]
7. Intro to Scikit-learn
   • pre-lecture: [ipynb], [colab]
   • post-lecture: [ipynb], [colab]
8. Deep learning with PyTorch
   • pre-lecture: [ipynb], [colab], [slides]

Content from previous offerings of the course:

• Winter 2019-2020
• Fall 2019-2020