Opinto-opas

Tavoitteet

Kurssin suoritettuaan opiskelija:
- Osaa kertoa, mitä data-analyysi ja koneoppiminen ovat
- Osaa kertoa miksi data-analyysiä ja koneoppimista käytetään
- Osaa analysoida ja visualisoida dataa
- Osaa kuvata koneoppimisprosessin
- Osaa käyttää soveltuvia työkaluja data-analyysiin ja koneoppimiseen

Sisältö

Johdatus data-analyysiin ja koneoppimiseen
Data-analyysin prosessi ja menetelmät
Koneoppimisen prosessi ja menetelmät
Käytännön harjoittelu

Oppimateriaalit

Course book:

Aurélien Géron.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2nd Edition.
Publisher : O'Reilly Media; 2nd edition
(October 15, 2019)

We read chapters 1-10 of the book of menus. They have about 300 pages, but some are skipped over.

The course book can be read in electronic form from our institution's eBook Central database.

The course also has reading material, which will be announced during the course.

Pedagogiset toimintatavat ja kestävä kehitys

The course includes approximately 12 guided working and theory sessions, 10 personal practice tasks and group work.
*
Group work is done in groups of 3-4 people outside of guidance sessions. The group sets aside 15 minutes outside of guidance sessions to present the group work.

Sisällön jaksotus

Introduction to machine learning:
- data exploration
- data processing and preparation
- model training, selection, and evaluation
- taking the model into production
- supervised learning
- unsupervised learning
- visualization

We proceed in general according to the chapters in the course book.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

The course is graded on a scale of 0-5.
*
In order to receive an approved performance, the student must receive an acceptable mark for both 1) personal practice tasks and 2) group work.
*
You can get at least 10 points for each practice task. You can therefore get a maximum of 100 points from all 10 practice tasks.
Personal practice tasks: 25 points -> grade 0.5; 38 -> 1.0; 50 -> 1.5; 63 -> 2.0; 75 - 2.5; 88 -> 3.0. The tasks are checked in the demos. Must be present at the demo sessions.
Participation in group work: 0.0 - 2.0.
*
(In both cases, 0.5 is the first accepted grade)