Course Lecturers: Mauricio A Álvarez, Mingfei Sun and Sara Summerton
This is a level six (MSc) module offered by the Department of Computer Science at the University of Manchester.
This is module 1 in the Fundamentals of Machine Learning theme. Machine Learning is concerned with creating mathematical "data structures" that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be spam filtering, speech recognition, medical diagnosis, or weather prediction. The data structures we use (known as "models") come in various forms, e.g. trees, graphs, algebraic equations, and probability distributions. The emphasis is on constructing these models automatically from data---for example making a weather predictor from a datafile of historical weather patterns. This course will introduce you to the concepts behind various Machine Learning techniques, including how they work, and use existing software packages to illustrate how they behave.
You can run the Jupyter Notebooks directly on Google Colab. Click on each Colab Badge to open the notebook.
| Date | Lab session | Google Colab link |
|---|---|---|
| October 7 | An end-to-end ML project |  |
| October 21 | Linear Regression, Part 1 | |
| Linear Regression, Part 2 | |
|
| November 11 | Neural Networks | |
If you want to save changes to the Notebook, you need to save them before quitting. According to this link:
If you would like to save your changes from within Colab, you can use the File menu to save the modified notebook either to Google Drive or back to GitHub. Choose File→Save a copy in Drive or File→Save a copy to GitHub and follow the resulting prompts. To save a Colab notebook to GitHub requires giving Colab permission to push the commit to your repository.