Machine Learning Part 2, 3 credits

Aims and objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall

• further develop the ability of students to integrate and make autonomous use of their knowledge
• develop the students' ability to deal with complex phenomena, issues and situations, and
• develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course objectives

Knowledge and understanding
Completing this course, the student will know about the different ways to preprocess data, ways to evaluate and improve the performance of learning systems, and ways to chose and apply different basic machine learning algorithms.

Applied knowledge and skills
Completing this course, the student will be able to prepare data and apply machine learning methods to achieve a learning goal within an intelligent system.

Making judgments and attitudes
Completing this course, the student will be able to judge the suitability of a machine learning paradigm for a given problem and the available data, and is able to identify problems or misleading results during the training process.

Main content of the course

• Applied Machine Learning
• Application of machine learning algorithms for the tasks of classification and prediction
• Methods for data preprocessing including normalisation, feature extraction, dimensionality reduction, and re-balancing
• Principal Component Analysis
• Practical recommendations for applying machine learning algorithms, and
• Bias-Variance Dilemma.

Teaching methods

The course is designed as a blended learning course including classroom events. It comprises a series of online lectures, obligatory self-study exercises and class-room seminar presentations of a case-based learning task.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Examination methods

Exercises, 2 credits (Code: A003)
Examination is done with individual written reports.

Case-based study, 1 credits (Code: A004)
Examination is done with an individual written report.

For students with a documented disability, the university may approve applications for adapted or other forms of examinations.

For further information, see the university's local examination regulations (in Swedish).

Grades

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

In accordance with university regulations regarding grading systems for first and second-cycle courses (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades shall be used: Fail (U), Pass (G) or Pass with Distinction (VG). For courses that are included in an international Master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the grading scale of A-F shall be used. The vice-chancellor, or a person appointed by the vice-chancellor, may decide on exceptions from this provision for a specific course, if there are special grounds.

Grades used on course are Fail (U), Pass (G) or Pass with Distinction (VG).

Exercises
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).

Case-based study
Grades used are Fail (U) or Pass (G).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

The final grade is set on test code A003, provided that A004 is approved.

Specific entry requirements

At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" from the Swedish Upper Secondary School. The applicant must also have Machine Learning Part 1, 3 credits.

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Other provisions

The course is given in English.

Reading list and other teaching materials

No Course Literature is required.