Applied Machine Learning

The course is mandatory within the Applied Data Science, Master's Programme (N2ADS).

The course can be part of the following programmes:

1. Computer Science, Master's Programme (N2COS)
2. Software Engineering and Management, Master's Programme (N2SOF)
3. Computer Science, Bachelor's Programme (N1COS)
4. Mathematical Sciences, Master's Programme (N2MAT)

The course is a also a single-subject course at Gothenburg University.

To be eligible to the course, the student should have a Bachelor's degree in any subject, or have successfully completed 90 credits of studies in computer science, software engineering, or equivalent. Specifically, the course requires:

• 7.5 credits programming,
• 7.5 credits introduction to data science or AI, such as DIT852 or DIT405,
• 7.5 credits calculus or mathematical modeling (such as DIT856),
• 7.5 credits probability theory, statistics, or mathematical statistics, such as DIT862. Alternatively have taken both of the following two courses: DIT847 and DIT278 (or equivalent)

Applicants must prove knowledge of English: English 6/English level 2 or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.

The course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications.

During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering.

The use cases and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. Methodological questions pertaining to the evaluation of machine learning systems will also be discussed, as well as some of the ethical questions that can arise when applying machine learning technologies.

There will be a strong emphasis on the real-world context in which machine learning systems are used. The use of machine learning components in practical applications will be exemplified, and realistic scenarios will be studied in application areas such as e-commerce, business intelligence, natural language processing, image processing, and bioinformatics. The importance of the design and selection of features, and their reliability, will be discussed.

After completion of the course the student should be able to:

Knowledge and understanding

• describe the most common types of machine learning problems,
• explain what types of problems can be addressed by machine learning, and the limitations of machine learning
• account for why it is important to have informative data and features for the success of machine learning systems,
• explain on a high level how different machine learning models generalize from training examples.

Competence and skills

• apply a machine learning toolkit in an application relevant to the data science area,
• write the code to implement some machine learning algorithms,
• apply evaluation methods to assess the quality of a machine learning system, and compare different machine learning systems.

Judgement and approach

• discuss the advantages and limitations of different machine learning models with respect to a given task,
• reason about what type of information or features could be useful in a machine learning task,
• select the appropriate evaluation methodology for a machine learning system and motivate this choice,
• reason about ethical questions pertaining to machine learning systems.

Lectures, exercise sessions, computer lab sessions.

Language of instruction: English

The course is examined by an individual written take-home examination, as well as mandatory written assignments submitted as written reports, some of which will be carried out individually and others in groups of normally 2-4 students.

Late submission of the take-home examination results in the grade Fail (U), unless special reasons exist. A failed take-home examination is reexamined by a new take-home exam.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

Sub-courses

1. Take-home examination, 4 credits
   Grading scale: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U)
2. Assignments, 3.5 credits
   Grading scale: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U)

The grading scale comprises: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U).

To pass the course, all mandatory components must be passed. To be awarded a higher grade (4 or 5) for the full course, you need to have passed both the sub-courses with a grade of 4 or 5, respectively.

The course is evaluated through meetings both during and after the course between teachers and student representatives. Further, an anonymous questionnaire is used to ensure written information. The outcome of the evaluations serves to improve the course by indication which parts could be added, improved, changed or removed.

The course is a joint course together with Chalmers.

The course replaces the course DIT865, 7.5 credits. The course cannot be included in a degree which contains DIT865. Neither can the course be included in a degree which is based on another degree in which the course DIT865 is included.

The course cannot be included in a degree which contains the course DIT381.