IN4050 – Introduction to Artificial Intelligence and Machine Learning

Schedule, syllabus and examination date

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Changes in the course due to coronavirus

Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.

See general guidelines for examination at the MN Faculty autumn 2020.

Course content

This course gives a basic introduction to machine learning (ML) and artificial intelligence (AI). Through an algorithmic approach, the students are given a practical understanding of the methods being taught, in particular through making their own implementations of several of the methods. The course covers supervised classification based on e.g., artificial neural networks (deep learning), as well as unsupervised learning (clustering), regression, optimization (evolutionary algorithms and other search methods) and reinforcement learning, in addition to 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.

Learning outcome

After taking the course, you will:

  • have good insight into the main methods used in machine learning (ML) and artificial intelligence (AI)
  • have knowledge of the historical development of the field and challenges by making more general intelligent systems
  • be able to consider the pros and cons when choosing ML / AI methods for different applications and problems
  • be able to design and conduct experiments using the methods, with emphasis on evaluation and comparison
  • be able to implement algorithms for selected methods and combine them into hybrid systems
  • get experience with different ways of using a data set for training and testing
  • have knowledge of basic philosophical and ethical issues related to the development and application of ML / AI

Admission to the course

Students at UiO register for courses and exams in Studentweb.

Some experience with programming, preferably including the course IN2010 – Algorithms and Data Structures.

Overlapping courses


2 hours of lectures and 2 hours of exercises each week.

Completion of mandatory assignments that will be more extensive that for the ´main course´ is compulsory. Read more about requirements for submission of assignments, group work and legal cooperation under guidelines for mandatory assignments.


The course has a 4 hour written digital exam, but might get an oral exam if the number of students is low.

All mandatory assignments must be approved to be allowed to take the exam.

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: IN3050 – Introduction to Artificial Intelligence and Machine Learning, INF3490 – Biologically inspired computing (continued) and INF4490 – Biologically Inspired Computing (continued).

Examination support material

No examination support material is allowed.

Grading scale

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.

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.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Nov. 28, 2020 6:16:26 AM

Facts about this course

Teaching language