EPN-V2

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
Weight
10.0 ECTS
Year of study
2023/2024
Curriculum
SPRING 2024
Schedule
Programme description
Course history

Introduction

The project plan, literature study, problem statement and research questions from Phase 1 forms the basis for Phase 2. The work is carried out under the guidance of the supervisor appointed at the start of Phase 1.

In addition to the project work, there will be a series of online, asynchronous classes during which students will be provided with a range of analytical tools and methods to help develop their writing skills. Students will also receive formative feedback on draft versions of their texts from the course instructor and their peers, with a focus on the Phase 2 report.

Recommended preliminary courses

  • Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
  • Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.

Required preliminary courses

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Learning outcomes

A-F

Content

This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various 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

Assistant professor Nuno Marques

Course requirements

Assessment is pass or fail.

Assessment

Two internal examiners. External examiner is used periodically.

Permitted exam materials and equipment

Assistant professor Safiqul Islam

Grading scale

This is the first phase of their research where the student can focus entirely on development and getting results for their project.

The academic writing workshops will cover topics such as

  • Variations in academic style
  • Audience, purpose and style
  • The writing process
  • Disciplinary identity
  • Academic language
  • Vocabulary, grammar, sentence, paragraph and text
  • Coherence and cohesion
  • Directness and formality
  • Avoiding common errors: e.g. digression, lack of thesis statement, misunderstanding one’s audience
  • Analysing, discussing and responding to academic texts
  • Article structures, including IMRAD

Examiners

Two internal examiners. External examiner is used periodically.

Course contact person

Associate Professor Raju Shrestha