732A96 · 6 credits · Advanced level (A1F) · Statistik
Advanced Machine Learning
Active · next start HT 2026
13%
passed the latest regular sitting
DAT2 · 28 Oct 2025 · 15 students
51%
passed the regular sittings, all sittings over the last 5 years
DAT2 · 43 students
Unusually hard in 2025/26
13% passed the ordinary exam in 2025/26 (15 students), against 71% on average in earlier years (28 students).
Other module
DAT2 Examination
- B
- C
- D
- E
- F
Main rounds
51% passed · 43 students
- 13%15
- 64%14
- 79%14
Later rounds
35% passed · 20 students
- 22%9
- 45%11
- Pass a later round: 35% (20 students), compared with 51% (43 students) on the ordinary exam
- Get B: 10% (63 students)
Show small sittings (4)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 25%4
- 0%2
- 100%1
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB2 Laboratory work
- P
Main rounds
100% passed · 23 students
- 100%12
- 100%11
Later rounds
100% passed · 12 students
- 100%12
- Pass a later round: 100% (12 students), compared with 100% (23 students) on the ordinary exam
Show small sittings (5)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%2
- 100%3
- 100%1
Hand-in or lab
LAB1 Laboratory work
- P
Main rounds
100% passed · 24 students
- 100%9
- 100%15
Later rounds
No sittings with at least 5 students.
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
Other module
DAT1 Examination
- A
- B
- C
- D
- E
- F
Main rounds
88% passed · 25 students
- 100%10
- 80%15
Later rounds
No sittings with at least 5 students.
- Get B or A: 52% (25 students)
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 0%1
- 0%1
- 33%3
About the course
Aim
After completing the course, the student shall be able to :
- account for the principles of machine learning used in the Bayesian tradition of machine learning,
- construct a suitable probabilistic model describing the data structure and the prior,
- compare between models in order to select the best one,
- implement machine learning models in a programming language and also use standard machine learning libraries in order to perform the model inference, make predictions based on these models and estimate the uncertainty of these predictions.
Content
The course covers some advanced methods in machine learning that allow for modelling complex phenomena and predicting the outcomes of these phenomena.
The following topics are included in the course:
- Introduction to Bayesian Learning: likelihood, prior, posterior, marginal likelihood, posterior predictive distribution.
- Generative and discriminative models,
- Gaussian process,
- State-space models,
- Kalman filtering and smoothing,
- particle methods,
- Markov models and hidden Markov models,
- graphical models, such as Bayesian networks and Markov random fields.
- Examiner
- Jose M Pena
- Main field of study
- Statistik