Algorithms for Machine Learning and Inference

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. Applied Data Science Master's Programme (N2ADS)
4. Game Design & Technology, Master's Programme (N2GDT)
5. 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 degree.

In particular, the student must have acquired the following knowledge:

• 7.5 credits of programming (e.g., DIT440 Introduction to Functional Programming, DIT042 Object-Oriented Programming, DIT012 Imperative Programming with Basic Object-Orientation, or equivalent)
• 7.5 credits of data structures (e.g., DIT961 Data Structures, DIT181 Data Structures and Algorithms, or equivalent)
• 7.5 credits of basic probability and statistics (e.g., MSG810 Mathematical Statistics and Discrete Mathematics, DIT861 Statistical Methods for Data Science, or equivalent) - 7.5 credits of linear algebra (e.g., MMGD20 Linear Algebra, or equivalent)
• 7.5 credits of multivariate calculus.

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

This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model.

Algorithms of this kind are commonly used in for example classification tasks (e.g., character recognition, or to predict if a new customer is creditworthy) and in expert systems (e.g., for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases.

The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature. During the course we may discuss potential problems with machine learning methods, for example, bias in training data and safety of autonomous agents.

The following concepts are covered:

• Bayesian learning: likelihood, prior, posterior.
• Supervised learning: Bayes classifier, Logistic Regression, Deep Learning (Standard and CNN), Support Vector Machines, regression models, K-nn models.
• Unsupervised learning: Clustering algorithms, EM algorithm, Mixture models,
• Kernel methods,
• Temporal machine learning models (for example RNN)

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

Knowledge and understanding

• explain a representative set of available methods for machine learning

Competence and skills

• implement and analyze machine learning algorithms
• apply sound mathematical principles to the inference of hypotheses from empirical data and models on scientific grounds

Judgement and approach

• choose appropriate methods and apply them to specific inference problems, based on a solid understanding of scientific literature in the field
• evaluate the methods qualitatively and quantitatively, and recognize their strengths as well as their limitations

Lectures and homework assignments.

Language of instruction: English

The course is examined by assignments and a written hall examination.

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. Written hall examination, 4.5 credits
   Grading scale: Pass with distinction (5), Pass with credit (4), Pass (3) and Fail (U)
2. Assignments, 3 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 earn a higher grade than Pass, a higher weighted average from the grades of the components is required.

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 DIT380, 7.5 credits. The course cannot be included in a degree which contains DIT380. Neither can the course be included in a degree which is based on another degree in which the course DIT380 is included.

The course cannot be included in a degree which contains DIT866.