Course syllabus

Course-PM

DAT341 / DIT867 DAT341 / DIT867 Applied machine learning lp4 VT25 (7.5 hp)

Course is offered by the department of Computer Science and Engineering

Contact details

Examiner and course responsible: Selpi

Schedule

TimeEdit

Course literature

The course does not follow a particular course book closely. Instead, we shall provide some pointers to relevant materials from different sources that students can read.

For the practical coding parts of the course, we shall provide links to the API documentation of the libraries that will be used. We shall also publish some Jupyter notebooks illustrating the usage of the libraries.

 

Course content

The course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications. During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering. The use cases and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. Methodological questions pertaining to the evaluation of machine learning systems will also be discussed, as well as some of the ethical questions that can arise when applying machine learning technologies.

There will be a strong emphasis on the real-world context in which machine learning systems are used. The use of machine learning components in practical applications will be exemplified, and realistic scenarios will be studied in application areas such as e-commerce, business intelligence, natural language processing, image processing,  bioinformatics, and others. The importance of the design and selection of features, and their reliability, will be discussed.

Changes made since the last occasion

Incremental changes are applied to some topics to reflect the development in the field. Some assignments are changed and/or revised.

Learning outcomes

On successful completion of the course the student will be able to:

Knowledge and understanding

  • describe the most common types of machine learning problems,
  • explain what types of problems can be addressed by machine learning, and the limitations of machine learning
  • account for why it is important to have informative data and features for the success of machine learning systems,
  • explain on a high level how different machine learning models generalize from training examples.


Skills and abilities

  • apply a machine learning toolkit in an application relevant to the data science area,
  • write the code to implement some machine learning algorithms,
  • apply evaluation methods to assess the quality of a machine learning system, and
  • compare different machine learning systems.


Judgement and approach

  • discuss the advantages and limitations of different machine learning models with respect to a given task,
  • reason about what type of information or features could be useful in a machine learning task,
  • select the appropriate evaluation methodology for a machine learning system and motivate this choice,
  • reason about ethical questions pertaining to machine learning systems.

 

Link to the syllabus on "Studieportalen".

 

Examination form

The final course grade (Fail, 3,4, or 5) is determined by:

  • the final assignment grade and
  • the exam grade.
Final assignment grade Final exam grade Final course grade
5 5 5
4 or 5 4 or 5 4
3 or higher 3 or higher 3

The exam will be take-home exam (4 credits); the information on how to get grade 3, 4, or 5 for the exam is given in the exam paper.

The final assignment grade is determined by the total sum of points from all graded assignments and that the students passed all assignments (see table below).

Non-graded assignments Graded assignments Final assignment grade

All must be passed

 All must be passed. Total sum of points from all graded assignments must be at least 85% of the total max point 5
All must be passed All must be passed. Total sum of points from all graded assignments must be at least 65% of the total max point 4
All must be passed All must be passed 3

If a student does not pass a particular assignment, a re-submission must be done as soon as possible but not later than 10 days after the exam. A re-submission of a graded assignment will only give a fail or a passing point; a re-submission is not for getting a higher grade.

General Rules and Policies

  • The deadlines for submitting assignments are firm. Delays must be motivated before the deadlines. Unannounced late submissions will not be considered.
  • It is allowed, even encouraged, to discuss the assignments during the course. Also, do not hesitate to ask if you have difficulties with the assignments, or if something is unclear.
  • You must write your final solutions on your own, using your own words, and expressing them in the way you understood them yourself.
  • Submitting others' work in your own name is cheating! It can lead to severe consequences, in very bad cases even suspension from studies.
  • Specifically, it is prohibited to copy (with or without modifications) from each other, from books, articles, web pages, etc., and to submit solutions that you got from other persons, unless you explicitly acknowledge the sources and add your own explanations. We will be particularly watchful if exercises appear as (alleged) innocent questions in internet forums.
  • You are also responsible for not giving others the opportunity to copy from your work. We will not investigate who copied from whom. 

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