AbstractThis course gives a fundamental introduction to machine learning (ML) with an emphasis on statistical aspects. In the course, we focus on both the theoretical foundation for ML and the application of ML methods.
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.
The course gives an overview of fundamental concepts and reasoning behind machine learning methods, based especially on a probabilistic and decision-theoretic framework. Further, we discuss a broad range of classical machine learning methods such as
- Linear models for regression and classification
- Neural networks
- Kernel methods
- Mixture Models and EM
- Ensemble methods
- Decision trees
The course is mandatory for third semester in the BSc in Data Science and assumes students to have followed the courses Applied Statistics and Linear Algebra and Optimisation, or something equivalent.
Some prior experience with Python will also be assumed.
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 convey the results.
The course will comprise around 10 weeks of lectures and exercise sessions and around 4 weeks of project work.
Aurélien Géron (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly.
Christopher Bishop (2006). Pattern Recognition and Machine Learning. Springer.
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 20%
- Lectures: 15%
- Exercises: 20%
- Project work, supervision included: 25%
- Exam with preparation: 20%
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.
A project report on the analysis of a dataset using machine learning methods.
Individual exam : Individual student presentation followed by an individual dialogue. The student is examined while the rest of the group is outside the room.