IN4050 – Introduction to Artificial Intelligence and Machine Learning
Schedule, syllabus and examination date
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.
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.
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.
Recommended previous knowledge
Some experience with programming, preferably including the course IN2010 – Algorithms and Data Structures.
- 10 credits overlap with IN3050 – Introduction to Artificial Intelligence and Machine Learning.
- 7 credits overlap with INF3490 – Biologically inspired computing (continued).
- 7 credits overlap with INF4490 – Biologically Inspired Computing (continued).
- 3 credits overlap with FYS-STK3155 – Applied Data Analysis and Machine Learning.
- 3 credits overlap with FYS-STK4155 – Applied Data Analysis and Machine Learning.
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.
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.