ACIT4630 Advanced Machine Learning and Deep Learning Course description

Course name in Norwegian
Advanced Machine Learning and Deep Learning
Study programme
Master's Programme in Applied Computer and Information Technology
Year of study
Course history


This course provides a broad introduction to machine learning (ML), which includes supervised, unsupervised, and reinforcement learning, and deep learning (DL) that can be used in different application domains. Students will learn both theories and practices in ML and DL. Moreover, students will learn from studying, presenting, and discussing relevant research articles and expose themselves to research by doing a research project.

Recommended preliminary courses

Bachelor level knowledge of the following topics is helpful for understanding some of the concepts in this course:

  • linear algebra
  • vector calculus
  • basic statistics and probability.

Some experience with programming, especially with Python, and any machine learning frameworks such as Keras, TensorFlow, and scikit-learn will be beneficial.

Required preliminary courses

No formal requirements over and above the admission requirements.

Learning outcomes

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills and general competence.



The student:

  • is knowledgeable about supervised, unsupervised, reinforcement learning
  • has a good understanding of the principles of state-of-the-art deep neural networks such as CNN, RNN, GAN, RL.
  • has a good understanding of both theoretical and practical know-how required to use machine and deep learning methods effectively



The student:

  • develop practical skills necessary to build, train, and deploy machine learning and deep learning models
  • is able to analyze machine learning methods in regard to their performance and effectiveness
  • is able to use existing deep learning networks, improve and/or customize them to apply to new problems


General competence

The student:

  • has both theoretical and practical understanding of machine and deep learning methods
  • can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
  • is able to work on relevant research projects


This course covers the fundamental principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in many areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.

Teaching and learning methods

The course consists of lectures, assignments, presentation seminars, and project work. Students will actively participate in the seminars by presenting papers, listening, and discussing on other presentations. The aim of the course is to provide a research-oriented education in the field. Students will do research projects with the aim of cultivating them towards good future researchers.


Practical training


Course requirements

The following required coursework must be approved before the student can take the exam:

  • Two oral presentations (one on a given topic, one on the topic of own choice)
  • Participate as a prepared opponent/discussant in two presenations from other students


The assessment will be based on:

  • A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course. 
  • Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.


New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Permitted exam materials and equipment

All aids are permitted for the project report.

No aids are permitted for the oral exam.

Grading scale

For the final assessment, a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.


Two internal examiners. External examiner is used periodically.

Course contact person

Associate Professor Raju Shresta