Official course description:
This is a complete 15 ECTS course on Machine Learning. Building on the math knowledge acquired from the course Linear Algebra and Probability, students will be introduced to Machine Learning during the first part of the course. In the second part, recent machine learning research will be addressed.
The course enables students to analyse machine learning algorithms, implement abstractly specified machine learning methods in an imperative programming language, modify machine learning methods to analyse practical datasets and convey the results.
Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; deep learning; reinforcement learning; kernel machines; clustering; graphical models; Bayesian estimation; ensemble methods, and statistical testing.
Linear Algebra and Probability
Intended learning outcomes
After the course, the student should be able to:
- Discuss, clearly explain, and reflect upon central machine learning concepts and algorithms.
- Choose among and make use of the most important machine learning approaches in order to apply (match) them to practical problems.
- Implement abstractly specified machine learning methods in an imperative programming language.
- Combine and modify machine learning methods to analyse practical dataset and covey 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 mandatory activities are weekly exercises that the student solve prior to the mandatory exercise session and/or home and will present the solution to the exercise class if randomly picked by the TA. In order to be qualified for the exam, the student must have solved and volunteered to present his/her solution to 50% of the mandatory problems on average. The completion rate is computed from the lists where the student check, prior to the exercise session, those problem he/she has solved and will be ready to present to the class. On the basis of the presented solution, the TA will give feedback and discuss the solution with the class and complement the solution if necessary. The mandatory weekly exercises occur in the first 10 weeks of the course and facilitate continuous learning throughout the course. The second attempt will be provided for the students, who do not pass the mandatories in the first attempt, after the first ten weeks of the course.
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.
Ethem Alpaydin: Introduction to Machine Learning, third edition. The MIT Press. ISBN 978-0-262-02818-9
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 6%
- Lectures: 18%
- Exercises: 18%
- Assignments: 18%
- Project work, supervision included: 30%
- Exam with preparation: 10%
Ordinary examExam type:
D: Submission of written work with following oral, Internal (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.