# Syllabus for Statistical Machine Learning

## Syllabus

• 5 credits
• Course code: 1RT700
• Education cycle: Second cycle
• Main field(s) of study and in-depth level: Technology A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Computer Science A1N, Data Science A1N
• Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
• Established: 2016-03-08
• Established by:
• Revised: 2023-02-08
• Revised by: The Faculty Board of Science and Technology
• Applies from: Autumn 2023
• Entry requirements:

120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and a course in introductory programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.

• Responsible department: Department of Information Technology

## Learning outcomes

On completion of the course, the student should be able to:

• discuss and determine if a technical problem described without specialized terminology can be formulated as a supervised machine learning problem and, if so, make this formulation.
• structure and divide supervised machine learning problems into tractable sub-problems, formulate a mathematical solution to the problems and implement this solution using statistical software.
• use and develop supervised machine learning models for classification and regression problems.
• describe the assumptions underlying supervised machine learning and the limitations that follow from these, including potential consequences for ethics and sustainability.
• analyse the quality of a model and use cross-validation for model selection and model tuning.
• explain important principles for generalization, including the trade-off between bias and variance, overfitting and underfitting.
• critically examine and provide constructive criticism on other students' reports about machine learning.

## Content

This is an introductory course in statistical machine learning, focusing on classification and regression: linear regression, regularization, logistic regression, discriminant analysis, classification and regression trees, ensemble methods, neural networks, deep learning; practical considerations such as cross validation, model selection, the bias-variance trade off, applying the methods to real data; ethical and sustainability considerations when using statistical machine learning.

## Instruction

Lectures, problem solving sessions (both with and without computer), laboratory work, feedback on written assignments including a minor project.

## Assessment

Written exam combined with oral and written presentation of assignments.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.