course-details-portlet

PROG2051 - Artificial Intelligence

About

Examination arrangement

Examination arrangement: School exam
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 100/100 4 hours D

Course content

The course focus on data-driven AI topics including machine learning, Bayesian networks, deep learning and we will look at popular and successful applications of these techniques in image processing and natural language processing. Practical applications and real world examples will be carefully walked through so that the students can follow and understand a complete AI project. Lab exercises, and obligatory assignments are important instruments to ensure learning progress with well-defined milestones.

This course replaces the original IMT3104 Artificial Intelligence.

Learning outcome

On successful completion of the module, students will be able to:

* Understand and evaluate various core techniques and algorithms of AI, including regression, machine learning, Markov decision process, and Bayesian networks. Understand the meaning of concepts such as intelligence, classification, clustering and decision-making.

* Identify different uses and applications of AI techniques and algorithms, from neuroscience, understanding brain to image processing, natural language processing, and other types of data different application domains.

* Implement several of the algorithms on different AI problems.

The students will also enhance their programming skills in a preferred language of their own by learning to program AI algorithms.

* Improve programming skills through the programming of AI algorithms. Programming exercises and assignments help enhancing the understanding the theory learnt in class.

* Evaluate the run-time and memory complexity of several AI algorithms, and practice with creating better algorithms.

Learning methods and activities

Lectures, lab exercises, self-study and obligatory assignments.

This course will focus on the practical implementation of AI concepts. Lectures will introduce a topic area, and students are expected to implement and report on the key concept.

Compulsory assignments

  • Mandatory assignments

Further on evaluation

*Evaluation* 100% Written exam in Inspera (there are several mandatory assignments that each student must finish before she/he is allowed to take the final exam).

For resit exams, the form is also 100% Written exam in Inspera.

Specific conditions

Compulsory activities from previous semester may be approved by the department.

Admission to a programme of study is required:
Computer Science (BIDATA)
Programming (BPROG)

Course materials

- History and overview of AI - Bayesian networks - Machine learning - Deep learning - Machine learning for image processing - Natural language processing - Other AI topics

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III

Coursework

Term no.: 1
Teaching semester:  SPRING 2023

Language of instruction: English

Location: Gjøvik

Subject area(s)
  • Informatics
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science

Examination

Examination arrangement: School exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 100/100 D INSPERA
Room Building Number of candidates
Summer UTS School exam 100/100 D INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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