Course syllabus adopted 2024-02-05 by Head of Programme (or corresponding).
Overview
- Swedish nameAvancerade teman i maskininlärning
- CodeDAT441
- Credits7.5 Credits
- OwnerMPDSC
- Education cycleSecond-cycle
- Main field of studyComputer Science and Engineering, Software Engineering
- DepartmentCOMPUTER SCIENCE AND ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 87137
- Maximum participants100
- Block schedule
- Open for exchange studentsNo
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0124 Written and oral assignments 3.5 c Grading: TH | 3.5 c | 0 c | 0 c | 0 c | 0 c | 0 c | |
| 0224 Examination 4 c Grading: TH | 4 c | 0 c | 0 c | 0 c | 0 c | 0 c |
|
In programmes
- MPALG - COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
- MPALG - COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 2 (elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
Examiner
Information missingGo to coursepage (Opens in new tab)Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
To be eligible for the course, a student must have passed a minimum of the following courses:- 7.5 credits of programming (Python experience desirable but not absolutely required)
- 7.5 credits of a data structures and/or algorithms
- 7.5 credits of basic probability and statistics
- 7.5 credits of calculus
- 7.5 credits of linear algebra
- 7.5 credits of basic machine learning (for example TDA233, MVE440, DAT340)
Aim
This course will focus on advanced topics in machine learning, in order to provide a deep understading of the modern machine learning areas. The students will learn sophisticated machine learning models which are commonly used in real-world applications, and also will learn how to analyze and understand in depth the advanced machine learning models.Learning outcomes (after completion of the course the student should be able to)
On successful completion of the course the student will be able: to learn about modern and advanced machine learning methods and analyze them in different situations
to read and understand state-of-the-art scientific articles in the field
to propose and employ suitable models for the complex machine learning tasks
to be prepared for research and development of advanced machine learning methods
Knowledge and understanding
to understand in depth the advanced machine learning methods, and learn their practical implications
to understand some of the main research areas and topics within machine learning
to learn the way the complex real problems are turned into research questions in machine learning
Competence and skills
to analyze advanced machine learning and understand why a method may work or may fail
to deal with the cases where the standard machine learning is not very helpful and needs to be improved
to be better prepared for research in AI/machine learning
to read and follow the relevant state-of-the-art research articles
Judgement and approach
to employ a suitable model w.r.t. the assumptions and analyze the different aspects such as performance and effectiveness
to learn how machine learning models can be further developed to satisfy the requirements
to distinguish some of the main research areas in machine learning, the corresponding challenges and the current approaches to solve them
Content
The course is focused on advanced theory, methods and mathematics of machine learning.The course content is the following:
Theoretical machine learning and the computational aspects
Sequential decision making
Online learning/Active learning/Multi-armed bandits
Markov decision processes
Reinforcement learning
Relevant advanced deep learning (Deep Neural Network) models
Organisation
Lectures and assignments.Literature
See course homepage.Examination including compulsory elements
The course is examined by assignments and a written hall examination.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.