IN3050 – Introduction to Artificial Intelligence and Machine Learning
This course gives a basic introduction to machine learning (ML) and artificial intelligence - AI. With an algorithmic approach, the students are given a practical understanding of the methods being taught, as well as through making their own implementation of several of the methods. The course covers supervised classification based on eg., artificial neural networks - deep Learning, as well as un-supervised learning - clustering, regression, optimizing and reinforcement learning, as well as design of experiments and evaluation. Students also receive an introduction to philosophical fundamental problems and ethical questions related to ML / AI, as well as the field's history.
After taking the course, you will:
- have insight into the main methods used in machine learning (ML) and artificial intelligence (AI)
- have knowledge of the historical development of the field
- be able to design and conduct experiments using the methods, with emphasis on evaluation
- be able to consider the pros and cons when choosing ML / AI methods for different applications
- are able to implement algorithms for selected methods
- have knowledge of basic philosophical and ethical issues related to the development and application of ML / AI
Formal prerequisite knowledge
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
- Mathematics R1 or Mathematics (S1+S2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies. Read more about special admission requirements (in Norwegian).
Recommended previous knowledge
Some experience with programming preferably including the course INF2220 – Algorithms and Data Structures (continued)
- 7 credits overlap with INF3490 – Biologically inspired computing
- 7 credits overlap with INF4490 – Biologically Inspired Computing
- 10 credits overlap with IN4050 – Introduction to artificial intelligence and machine learning
2 hours of lectures and 2 hours of exercises (computer lab) each week.
Submission and approval of mandatory assignments are required.
The course has an oral exam, but might get a 4 hour written digital exam if the number of students is high. All mandatory assignments must be approved to be allowed to take the exam.
Examination support material
No examination support material is allowed.
Language of examination
The examination text is given in English.You may submit your response in Norwegian, Swedish, Danish or English.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
Resit an examination
Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.
Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN4050, INF3490 and INF4490.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.