Embedded Files
This module includes four lessons and it is an introduction to both Python programming and Machine Learning.
Introduction to Python programming: Programming developer environment, Colab Notebooks, write your first Python code: variable assignment, print(), input(), write and run simple Python scripts.
Introduction to ML: Introduction to the very first concepts of ML. ML for biological data: usefulness and challenges, The relationship between AI and Machine Learning, Introduction to data science. How ML works. A brief history of data.
Materials
Materials
Lesson 1
Lesson 1
0-introduction_to_the_progr&ML_course.pptx
Computer programming once had much better gender balance than it does today. What went wrong?
Lesson 2
Lesson 2
1-introduction_to_python (Google slides)
Introduction to Python
Introduction to Python
This lecture provides a gentle introduction to Python for students with no prior programming experience. It begins by explaining what programming languages are and how they work — from machine code and binary, through the spectrum of low- and high-level languages, to the role of interpreters and compilers. It then focuses on Python specifically: how to download and run the interpreter, the different environments available for writing code (terminal, code editors, IDEs, and Jupyter Notebooks), and the historical context of Python 2 vs Python 3. The lecture closes with a broader reflection on why so many programming languages exist and what makes Python particularly well suited to data analysis and machine learning. No prior programming knowledge is required or assumed.
Lesson 3
Lesson 3
2-introduction_to_ML (Google slides)
Introduction to Machine Learning
Introduction to Machine Learning
This lecture introduces the fundamental concepts underpinning Machine Learning (ML), placing it within the broader landscape of Artificial Intelligence and Data Science. Starting from the limitations of rule-based programming, it builds an intuitive understanding of what ML is and why it is needed — using concrete examples to illustrate the shift from explicitly programmed functions to models learned from data. The lecture also covers the nested relationship between AI, ML, and Deep Learning, and discusses why biological data in particular makes ML both necessary and powerful. No prior ML knowledge is assumed.
Lesson 4
Lesson 4
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