Advanced Python and Machine Learning ONLINE

Aims

Python is the programming language of choice when implementing Machine Learning projects. Having some basic knowledge typically enables you to use powerful libraries, but you might struggle with details and pitfalls. With this course, we aim to extend your Python skills to make more proficient use of Machine Learning. In the lectures, we will explore different Machine Learning topics and complete and execute small program snippets during the classes. You will be able to apply your new knowledge in additional practical assignments. This course is meant for people with solid basics in Python and little to no knowledge of Machine Learning and will give you the tools to use advanced Python and Machine Learning techniques in your projects.

Content

We will cover 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

Methods

We will make use of Jupyter notebooks in this course. The course material is going to be provided prior to the classes. Participants can use their own computers on the course days. Videos on how to install your own Python environment will be shared prior to the course. As an alternative, online services can be used which allow running Python scripts within a browser (no further installations required). A setup with two monitors (one to follow the presentation and another to work with the course material) is advisable.

As a central part of the course, the instructors will present different concepts and participants will be able to examine and adjust the course scripts themselves. We will make use of breakout rooms for exercises, in which participants can exchange and solve exercises jointly. Additionally, there will be 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 six years of experience as a researcher in Machine Learning.

Workload

Preliminary work 2h
Course attendance 16h
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.