The course takes place in period

  • 1 (2021-08-01 to 2021-10-24)
  • 2 (2021-10-25 to 2021-12-31)

Level/category

Professional studies

Teaching language

English

Type of course

Compulsory

Cycle/level of course

Second

Recommended year of study

1

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.

Study activities

  • Lectures - 30 hours
  • Individual- and group instruction - 7 hours
  • Project- and production work/artistic activities - 30 hours
  • Individual studies - 60 hours

Workload

  • Total workload of the course: 127 hours
  • Of which autonomous studies: 127 hours
  • Of which scheduled studies: 0 hours

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 and homeworks with associated content.

Teacher

  • Espinosa Leal Leonardo
  • Majd Amin
  • Scherbakov-Parland Andrej

Examiner

Espinosa Leal Leonardo

Group size

No limit (46 students enrolled)

Assignments valid until

12 months after course has ended

Course enrolment period

2021-08-09 to 2021-09-05

Room reservations
Date Time Room Title Description Organizer
2021-10-07 13:00 - 18:00 Machine Learning for Predictive Problems Espinosa Leal Leonardo
Majd Amin
Scherbakov-Parland Andrej
2021-10-08 13:00 - 18:00 Machine Learning for Predictive Problems Espinosa Leal Leonardo
Majd Amin
Scherbakov-Parland Andrej
2021-10-21 13:00 - 18:00 Machine Learning for Predictive Problems Espinosa Leal Leonardo
Majd Amin
Scherbakov-Parland Andrej
2021-10-22 13:00 - 18:00 Machine Learning for Predictive Problems Espinosa Leal Leonardo
Majd Amin
Scherbakov-Parland Andrej
2021-11-04 13:00 - 18:00 Machine Learning for Predictive Problems Espinosa Leal Leonardo
Majd Amin
Scherbakov-Parland Andrej
2021-11-05 13:00 - 18:00 Machine Learning for Predictive Problems Espinosa Leal Leonardo
Majd Amin
Scherbakov-Parland Andrej

Course and curriculum search