The course is included in these curricula and study modules
The course takes place in period
- 1 (2019-08-01 to 2019-10-27)
- 2 (2019-10-28 to 2019-12-31)
Level/category
Teaching language
English
Type of course
Compulsory
Cycle/level of course
First
Total number of ECTS
5 cr
Competency aims
The aim of the course is that the student will be
able to apply machine learning models and create a
proper pipeline for prediction from static data.
Learning outcomes
At the end of the course the student is expected
to be able to understand and predict with data
using machine learning models. The student will
be able to select the proper model and metric
based in the nature of the problem and optimize
its parameters using pipelines with grid-search.
Course contents
The students learn to understand the bases of
predictive machine learning with static data.
The students understand how modeling is
performed and how to deal with problems of
classification and regression. The students gain
an understanding of how to create optimized
models able to escalate to production.
Recommended or required reading
Introduction to Machine Learning with Python A
Guide for Data Scientists by Sarah Guido,
Andreas Müller.
An Introduction to Statistical Learning with
Applications in R by Gareth James, Daniela
Witten, Trevor Hastie and Robert Tibshirani,
Springer.
The Elements of Statistical learning by by
Trevor Hastie, Robert Tibshirani, Jerome
Friedman, Springer.
Deep Learning with Python by François Chollet,
Manning publications.
Mode of Delivery
Multiform education
Assessment requirements
To pass the course the student should pass the
following examinations: Jupyter notebooks solving a
problem during the lectures, quizzes based in
specific readings.
Teacher
- Akusok Anton
- Espinosa Leal Leonardo
- Scherbakov-Parland Andrej
Examiner
Westerlund Magnus
Home page of the course
Group size
No limit (46 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2019-08-12 to 2019-09-08
Date | Time | Room | Title | Description | Organizer |
---|---|---|---|---|---|
2019-10-17 | 13:00 - 18:00 | F249 | Machine Learning for Predictive Problems | Akusok Anton Espinosa Leal Leonardo |
|
2019-10-18 | 13:00 - 18:00 | F249 | Machine Learning for Predictive Problems | Akusok Anton Espinosa Leal Leonardo |
|
2019-10-31 | 13:00 - 18:00 | B518 | Machine Learning for Predictive Problems | Akusok Anton Espinosa Leal Leonardo |
|
2019-11-01 | 13:00 - 18:00 | F249 | Machine Learning for Predictive Problems | Akusok Anton Espinosa Leal Leonardo |
|
2019-11-14 | 13:00 - 18:00 | D4106 | Machine Learning for Predictive Problems | Akusok Anton Espinosa Leal Leonardo |
|
2019-11-15 | 13:00 - 18:00 | F249 | Machine Learning for Predictive Problems | Akusok Anton Espinosa Leal Leonardo |