Advanced Python and Machine Learning ONLINE

Aims

Python is the programming language of choice for implementing Machine Learning projects. While a basic understanding of Python allows you to tap into powerful libraries, you still might struggle with the details and common pitfalls. This course is designed to strengthen your Python skills and help you apply them more effectively in the context of Machine Learning. In the lectures, we explore key Machine Learning concepts and practice by writing and running small program snippets in class. You can apply your new knowledge through additional practical assignments. The course is intended for participants who already have a solid foundation in Python and little to no prior exposure to Machine Learning. By the end, you will be equipped with the skills to apply advanced Python techniques and Machine Learning methods in your own projects.

Content

We will explore the following topics:
* Advanced concepts in Python, like functions and classes, generators, lambda functions, decorators, and more
* Powerful libraries like scikit-learn, Numba, Keras, TensorFlow, and others
* Machine Learning concepts for regression, classification, and knowledge discovery including:
- Standard regression techniques, such as ridge and logistic regression
- Decision trees and Random Forests
- Dimensionality reduction with principal component analysis
- Clustering
- Gaussian processes and kernel methods
- Typical neural network models, such as convolutional neural networks and variational autoencoders
* Practical real-world examples
* Employing advanced coding environments and AI-based assistance: VS Code, PyCharm, GitHub Copilot

Methods

Throughout the course, we will work with Jupyter notebooks. Course material will be made available in advance, and participants can use their own computers during the course days. Videos on setting up an own Python environment will be shared beforehand. Alternatively, participants may use the online platform Renku to run Python directly in the browser, requiring no installation. For the best experience, a dual-monitor setup is recommended (one screen to follow the presentation and another to work with the course material).

A key element of the course is the instructor’s live presentation of programming concepts, during which participants will have the opportunity to explore, modify, and experiment with the provided scripts. We will use breakout rooms for the exercises, enabling participants to collaborate, exchange, and solve tasks together. Additionally, there will be (optional) programming assignments to be completed after the classes.

Target Group

All Doctoral Candidates & Postdocs

About the Trainer

Maxim Samarin is a Senior Data Scientist at the Swiss Data Science Center. Maxim holds a PhD in Computer Science / Machine Learning and has more than eight years of experience as a researcher in Machine Learning.

Workload

Preliminary work: 2h
Course attendance: 4x 4h
Assignments: 3x 4h (optional to obtain 1 ECTS)

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.