Course - Artificial Intelligence - PROG2051
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
- 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
Admission to a programme of study is required:
Computer Science - Engineering (BIDATA)
Programming (BPROG)
Recommended previous knowledge
- BMA1010 or IMAG2021
- IDATG 2102
Course materials
- History and overview of AI - Bayesian networks - Machine learning - Deep learning - Machine learning for image processing - Natural language processing - Other AI topics
No
Version: 1
Credits:
7.5 SP
Study level: Third-year courses, level III
Term no.: 1
Teaching semester: SPRING 2025
Language of instruction: English
Location: Gjøvik
- Informatics
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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"