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
   • Lecture Notes: [colab]
2. Object-Oriented Programming (OOP) in Python
   • Lecture Notes: [colab]
3. Introduction to Numpy
   • Lecture Notes: [colab]
4. Linear Algebra with Numpy
   • Lecture Notes: [colab]
5. Introduction to Pandas
   • Lecture Notes: [colab]
6. Introduction to Scipy
   • Lecture Notes: [colab]
7. Introduction to Scikit-learn
   • Lecture Notes: [colab]
   • Additional Resource: [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (Github Notebooks)]
8. Deep Learning with PyTorch
   • Lecture: [colab]
   • Brief introduction of neural networks: [slides]

Content from previous offerings of the course:

• Spring 2024
• Winter 2025