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Applied Machine Learning
Second title: Formerly "Big Data Analysis"
Provider: Faculty of Science

Activity no.: 5896-22-11-31
Enrollment deadline: 18/04/2022
PlaceNiels Bohr Institute
Date and timeApril 2022 - June 2022
Regular seats20
ECTS credits7.50
Contact personJulie Meier Hansen    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
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

 

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


Formel requirements

Sign up:

This course is offered to MSc and PhD students.

PhD students please sign up for the course using the credit student application » at this link. The course code to enter is NFYK20002U

For questions or problems with sign-up, please contact Julie Meier Hansen



It is expected that the student brings a laptop.


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.

Literature
See Absalon for final course material.

Target group

This course is offered to MSc and PhD students. For full course description and MSc sign-up >> link her

For PhD students, more info below.


Lecturers
See Absalon for final course material.

Remarks

Sign up: If you are a PhD student, please sign up for the course as a credit student » at this link. The course code to enter is NFYK20002U.

Contact Julie Meier Hansen (juliemh@nbi.ku.dk) if you have questions or problems re. sign-up.

 

 


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