Official course description:
course introduces Machine Learning methods and how they are used in active
research. Methods will be described in a way to enable you to select the
suitable tool for given application, and adapt accordingly.
DescriptionWe build upon knowledge from the course "Linear Algebra and Probability", and assume that you have prior knowledge in programming. It is highly recommended to have taken the course "Introduction to Machine Learning".
The course enables students to understand and implement advanced machine learning algorithms, and modify the learnt methods to analyze the outcome of various applications on different datasets. This includes the ability to select appropriate tools and reflect on the results.
The first 10 weeks of the course consist of lectures and exercises twice a week, the remaining 4 weeks are reserved for the mandatory group project, which will be part of the oral exam.
Linear Algebra and Probability
Intended learning outcomes
After the course, the student should be able to:
- Define and describe basic machine learning terms and methods.
- Develop and implement machine learning methods on your own in an appropriate programming language.
- Characterize, relate, and analyze central machine learning concepts and algorithms.
- Combine and modify machine learning methods to analyze practical datasets and reflect on the results.
The first 10 weeks will consist of lectures and exercise sessions. The last 4 weeks will consist of project work where students will implement machine learning methods from a recent research article.
The first 10 weeks, each week there will be one mandatory assignment related to the material of the week. You have to pass 50% of the assignments to be admitted to the exam. During the exercises it is mandatory to present your solutions. The TA will select the presenter among a set of volunteers on a fair basis. On the basis of the presented solution, the TA will give feedback and discuss the solution with the class and complement it if necessary.
The students who did not pass the mandatory 50% after the 10 weeks of the course for good reasons, i.e. failed the first attempt, can request a second attempt from us.
If the mandatory activities are not approved, the student will receive the grade NA (not approved) at the ordinary exam, and will have used an exam attempt.
The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
The following list is TENTATIVE. DO NOT BUY YET
[DL] Deep Learning, Goodfellow, 2017
[PML] An Introduction to Probabilistic Machine Learning, 2022, Murphy
[PML2] Probabilistic Machine Learning: Advanced Topics, 2023, Murphy
[PR] Pattern Recognition and Machine Learning, Christopher Bishop
Additional material will be provided during the lecture.
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 37%
- Lectures: 10%
- Exercises: 10%
- Assignments: 17%
- Project work, supervision included: 16%
- Exam with preparation: 10%
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
The project will take four weeks of the course where the students will work in groups to implement and evaluate a machine learning method from a recent research article.
The group makes their presentations together and afterwards the students participate In the dialogue individually while the rest of the group is outside the room.
- Group size: 2-4 students
Mixed exam 1 : Individual and joint student presentation followed by an individual and a group dialogue. The students make a joint presentation followed by a group dialogue. Subsequently the students are having individual examination with presentation and / or dialogue with the supervisor and external examiner while the rest of the group is outside the room.