Institut für Mathematik

Vorlesungen/Details

An introduction to machine learning

Dozent: Ashkan Nikeghbali


ADDITIONAL (PARALLEL) EXERCISE CLASSES

We have established three parallel exercise classes on three different dates. See below for details.

MAILING LIST

We want to establish a mailing list that will be used for urgent information about the course. If you are not a UZH student officially registered (e.g. you are from ETH and/or you're a ZGSM PhD student) send an email to gabriele.visentin@math.uzh.ch asking to be added to the mailing list.

SOME INFORMATION ON EXERCISE CLASSES

The first two classes will cover basic Python programming and some related packages (numpy, matplotlib, scikit-learn). Classes are meant to be interactive, therefore you should bring your own laptop with Python 3 pre-installed.
We suggest for first-time users to install Python 3 using the anaconda distribution (https://www.anaconda.com/distribution/).

Vorlesungen

Di 10.15 - 12.00
Raum: Y17M05

Übungen

Mi 08.00 - 09.45
Y27H28
Exercises An introduction to machine learning Gr.2
Tutor: Marcel Fenzl
Do 15.00 - 17.00
Y13M12
Exercises An introduction to machine learning Gr.3
Fr 10.15 - 12.00
Y27H28
Exercises An introduction to machine learning Gr.1
Tutor: Gabriele Visentin

Downloads



HOW TO USE THESE NOTEBOOKS?

No special software is needed!
Download the notebook from the link above. Your browser may mistaken the file for text and open it in a new browser window. In order to avoid this and in order to actually download the file, you must right-click on the link and select "Save linked content as" or "Save link as" or a similar item (the actual name depends on your browser and language), then download it in a folder in your computer.
Go to the website https://jupyter.org/try and click on "Try JupyterLab". A demo notebook will open in your browser (give it some time to load). Upload the Notebook you just downloaded and open it.

For a more comfortable experience, please consider downloading Jupyter from their website and install the software on your computer.
Notice that if you installed Python through Anaconda, Jupyter is already installed on your machine.

In case you have problems with Jupyter (either online or installed), you can download the pdf copy of the notebook above, but you will lose the interactivity of the code.

Prüfung

Modul: 20.01.2020 , Raum: n.n., Typ: n.n.
Project - details follow from lecturer

Modul: MAT003 An introduction to machine learning