Login for PhD students/staff at UCPH      Login for others
Applied Machine Learning
Second title: Formerly "Big Data Analysis"
Provider: Faculty of Science

Activity no.: 5896-21-11-31
Enrollment deadline: 19/04/2021
PlaceNiels Bohr Institute
Date and time26.04.2021, at: 00:00 - 27.06.2021, at: 16:00
Regular seats20
ECTS credits7.50
Contact personJulie Meier    E-mail address: juliemh@nbi.ku.dk
Enrolment Handling/Course OrganiserTroels Christian Petersen    E-mail address: petersen@nbi.ku.dk
Semester/BlockBlock 4
Scheme groupC
Exam detailsWritten assignment, final project Continuous assessment The final grade is given based on the continuous assessment consisting of three assignments (40%) as well as on the final project (60%).
Exam aidsAll aids allowed
Criteria for exam assessmentsee Learning Outcome
Exam re-examinationThe re-exam form will be oral based on a new or re-submitted final project (30 minutes, no preparation) counting for 60% of the final grade. If the assignments (continuous evaluation, 40%) were already approved during the course, they can be re-used, or new assignments can be submitted. Project and assignments must be submitted no later than 3 weeks before the re-exam.
Course workload
Course workload categoryHours
Lectures56.00
Preparation22.00
Theory exercises28.00
Project work100.00

Sum206.00


Content
The course will give the student an introduction to and a basic knowledge on Machine Learning (ML) and its use in various parts of data analysis. The focus will be on application through examples and use of computers, and be project based.

The course will cover the following subjects:
•Introduction to Machine Learning
•Types of problems suitable for ML and their typical solutions.
•Types of problems not suitable for ML
•Classification and Regression
•Supervised vs. Unsupervised training
•Dimensionality Reduction
•ML performance
•Big Data management and data access

Formel requirements




Learning outcome

 

Skills

The student should in the course obtain the following skills:
•Understand the use of ML in data analysis
•Use ML on a given (suitable) dataset
•Be able to optimise the performance of the ML algorithm
•Be capable of quantifying and comparing ML performances

Knowledge


The student will obtain knowledge about ML concepts and procedures, more specifically:
•The fundamental methods used in ML.
•Various Cost-Functions and Goodness measures.
•The most commonly used ML algorithms.

Competences


This course will provide the students with an understanding of ML methods and knowledge of (structured) data analysis with ML, which enables them to analyse data using ML in science and beyond. The students should be capable of handling data sparcity, non-uniformities, and categorical data.


Remarks
The course is identical to NFYK18000U Big Data Analysis. It is not allowed to pass both courses.

It is expected that the student brings a laptop.

Sign up:
The course is offered for both M.Sc. and PhD students. If you are a PhD student, please sign up for the course as a credit student via the following link: Applied Machine Learning - 2020/2021 (ku.dk)


General information for credit students and link to application » at this link


Search
Click the search button to search Courses.


Course calendar
See which courses you can attend and when
JanFebMarApr
MayJunJulAug
SepOctNovDec



Publication of new courses
All planned PhD courses at the PhD School are visible in the course catalogue. Courses are published regularly.