PCS956 Forskningstrender i anvendt maskinlæring

Upon completion, students will demonstrate proficiency in:

Knowledge

• explain the mathematical frameworks underlying machine learning methods that covered in the course, as well as how these methods are applied in selected application domains.
• problems assessment to select and apply suitable ML methods and software tools to solve them.
• some practical skills include designing strategies and implementing ML solutions.

Skills

• design solution strategies for machine learning problems,
• implement machine learning based solutions for some of the application domains that covered in the course.

General competence:

• develop solutions for real-life problems,
• work in multidisciplinary environment,
• technical communication.

In addition to meeting the general PhD program admission requirements, students should possess:

• Background in linear algebra and probability/statistics
• Familiarity with fundamental machine learning concepts
• Proficiency in Python programming, particularly with data science libraries

Note: Students with gaps in these prerequisite areas are welcome to enroll but they need to dedicate additional time to self-study. We recommend:

1. Reviewing foundational machine learning concepts through online resources (e.g., Stanford's CS229 Machine Learning, MIT Introduction to Machine Learning, etc.)
2. Strengthening Python programming skills.

The course assumes these foundational skills to focus on advanced topics and current research developments.

Assignment (e.g. project report and presentation): All support material is permitted

Oral exam: No support material