PCS956 Research Trends in Applied Machine Learning

Contents and structure

In this course, students delve into the dynamic field of machine learning (ML), where algorithms enable computer systems to glean insights from extensive datasets. ML is pivotal in technological advancements across a multitude of sectors, including large language models (LLM), computer vision, personalized recommendation engines, advanced web search algorithms, robotics, and data-driven analytics for scientific data interpretation. The course is attuned to the latest trends and applications in machine learning, emphasizing practical areas in four module: optimization, time-series, deep learning, and causal inference.

Acknowledgment:

This course is developed by a partial support from the RCN-INTPART DTRF Project.

Learning Outcome

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.

Entry requirements

• General admission criteria for the PhD programme.
• A solid background in linear algebra, statistics, machine learning basics, and proficiency with Python programming.

If students lack above skills, they may need to do more efforts toward the course completion.

Recommended previous knowledge

Having elementary-level coursework on machine learning or statistical inference is highly recommended.

Teaching methods

Lectures, e-learning materials, discussions, and projects.

Compulsory learning activities

Student activity expected during lectures, seminars, and workshop activities.

Assessment

The course is graded pass/fail based on assignments (including project's report) and an oral exam. Each of the two components must result in a pass grade in order to obtain a pass grade for the entire course.

Examination support material

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

Oral exam: No support material

More about examination support material