Advanced Project Course - AI and Machine Learning, 6 credits (TDDE19)

Avancerad projektkurs: AI och maskininlärning, 6 hp

Main field of study

Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Cyrille Berger

Director of studies or equivalent

Peter Dalenius
Course offered for Semester Period Timetable module Language Campus VOF
6CDDD Computer Science and Engineering, M Sc in Engineering 9 (Autumn 2017) 1, 2 4, 4 English Linköping v
6CITE Information Technology, M Sc in Engineering 9 (Autumn 2017) 1, 2 4, 4 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering 9 (Autumn 2017) 1, 2 4, 4 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2017) 1, 2 4, 4 English Linköping o
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2017) 1, 2 4, 4 English Linköping o
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2017) 1, 2 4, 4 English Linköping o
6CDDD Computer Science and Engineering, M Sc in Engineering (Systems Technology) 9 (Autumn 2017) 1, 2 4, 4 English Linköping v
6CITE Information Technology, M Sc in Engineering (Systems Technology) 9 (Autumn 2017) 1, 2 4, 4 English Linköping v
6MDAV Computer Science, Master's programme 3 (Autumn 2017) 1, 2 4, 4 English Linköping v
6MICS Computer Science, Master's programme 3 (Autumn 2017) 1 4 English Linköping v
6MICS Computer Science, Master's programme 3 (Autumn 2017) 2 4 English Linköping v

Main field of study

Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Computer Science and Engineering, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Computer Science, Master's programme

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

The course expects the student to have applied project management models in previous courses or other context. The student should also have acquired knowledge equivalent to basic courses in the profile "AI and machine learning" or "Systems Technology" or the specialization "AI and data mining" in the area covered by the project. 
 

Intended learning outcomes


The project should have significant technical level that requires in-depth subject knowledge in artificial intelligence and machine learning, should be carried out in a professional manner, and should develop and consolidate the participants' skills in the following areas:

  • Analyze and structure problems in the area of artificial intelligence and machine learning.
  • Apply knowledge and methods from a wide range of previous courses in the areas of artificial intelligence and machine learning.
  • Independently acquire new knowledge, as required by the project.
  • Integrate knowledge from many disciplines and apply them in the context of artificial intelligence and machine learning.
  • Formulate a requirement specification for the project based on a project directive and thereby assess the feasibility of the project in terms of technical solutions and available resources.
  • Present the project results for teh client as well as for other students, which can not be presumed to be specialists in the techniques used.
  • Actively contribute to a well functioning project group.
  • Demonstrate the ability to lead the project work with the support of a project model, and with limited access to supervisory resources.
  • Plan, implement and monitor a project in the area of artificial intelligence and machine learning.

The result of the project work should:

  • Attain high technical quality and be based on modern knowledge and practices in the relevant field of technology.
  • Be documented in relevant project documents and relevant technical documentation.
  • Be presented orally.
  • Meet the requirements stated in the specification.

Course content

Description of the projects, with project directives, are available on the course website. The projects will be closely linked to either ongoing research within the field of computer science or to companies active in this field. Examples could be develop a robotic system to perform some specific type of tasks, develop a system that learns to detect and track objects from sensor data, develop a recommender system for a specific domain, develop a system that learns to predict the activity of an object based on prior observations. The nature of the projects may change from year to year.
 

Teaching and working methods

The project, which is formed according to directive given later, should consist of at least six students. Each group will be assigned a supervisor, who will support the group in its work and answer technical questions. For each project, there is a client with whom the project team negotiates a specification. Before project work begins, the project team should create appropriate project management documents for the project.
For each instance of the course, the examiner will present a set of project proposals. Assignment of projects to student groups is based both on their aptitude and their wishes. For each proposal there is a project charter forming the basis for further work. The project begins with the project team developing a requirements specification and relevant project management documentation for their project. The projects should be conducted according to an appropriate development model, selected by the team.
The course runs over the entire autymn semester.
 

Examination

PRA1ProjectU, G6 credits

The project work will be assessed on the achievement of course objectives. Three modules, each assessed with pass/fail, are included in the assessment. These topics are:

  • Technical level and quality of project results
  • Written documentation in the form of technical report and relevant project documents
  • Oral presentation

To pass the whole project work, the student is required to pass all parts and meet the objectives of the course. Special emphasis is given to participants actively contributing to the group working according to the project model's intentions.
Grades are given as ”Fail” or ”Pass”.

Grades

Two-grade scale, U, G

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Peter Dalenius

Examiner

Cyrille Berger

Education components

Preliminär schemalagd tid: 64 h
Rekommenderad självstudietid: 96 h

Course literature

Kompletterande litteratur

Övrigt

Project specific

Additional literature

Other

Project specific

PRA1 Project U, G 6 credits

The project work will be assessed on the achievement of course objectives. Three modules, each assessed with pass/fail, are included in the assessment. These topics are:

  • Technical level and quality of project results
  • Written documentation in the form of technical report and relevant project documents
  • Oral presentation

To pass the whole project work, the student is required to pass all parts and meet the objectives of the course. Special emphasis is given to participants actively contributing to the group working according to the project model's intentions.
Grades are given as ”Fail” or ”Pass”.

Regulations (apply to LiU in its entirety)

The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva. 

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