Statistics, Machine Learning for Data Science, Second Cycle, 5 credits

Learning outcomes

Knowledge and understanding

After completing the course, the student shall have
• knowledge of the fundamental principles, types of algorithms and concepts
in machine learning,
• an understanding of the relationship between machine learning and statis-
tical learning and modelling, including their complementary roles in data
science.

Competence and skills

After completing the course, the student shall be able to
• apply both basic supervised and unsupervised learning algorithms, as well
as advanced machine learning concepts,
• evaluate and compare models based on appropriately selected perfor-
mance criteria.

Judgement and approach

After completing the course, the student has the ability to
• choose how to design, analyze, and present applications to case studies of
machine learning for data science.

Content

• Fundamental principles of prediction in machine learning
• Supervised, Unsupervised and Ensemble learning
• Representations and features
• Optimization behind learning algorithms
• Generalization of machine learning models
• Neural Networks and Deep learning
• Datasets for machine learning
• Interpretability and Explainability

Examinations and grades

Assignments, 5 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

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 determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

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

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Modes of assessment

Assignments, 5 credits (Provkod: A001)
Written and oral presentation

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

For further information, see the university's local examination regulations.

Specific entry requirements

First-cycle courses of 90 credits in Statistics, alternatively first-cycle courses of 30 credits in statistics and 60 credits in mathematics, alternatively first-cycle courses of 60 credits in statistics including 7.5 credits of Statistical theory and 7.5 credits of Regression analysis/Econometrics. The applicant must also have the courses Inference Theory, Second Cycle, 5 credits, Econometrics, Second Cycle, 7.5 credits and Computer Science, Programming for Statisticians, Second Cycle, 5 credits as well as qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations.

Other provisions

The course is given in English.

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.

Reading list and other learning resources

Required Reading

Moritz Hardt and Benjamin Recht (latest edition)
Patterns, Predictions, and Actions: Foundations of Machine Learning
Princeton University Press, (freely available online)

Trevor Hastie, Robert Tibshirani and Jerome Friedman (latest edition)
The Elements of Statistical Learning
Springer, (freely available online)

Additional material handed out.

Reference Material

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibsharani, and Jonathan Taylor (latest edition)
An Introduction to Statistical Learning: with Applications in Python
Springer, (freely available online)

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharani (latest edition)
An Introduction to Statistical Learning: with Applications in R
Springer, (freely available online)