PCS956 Research Trends in Applied Machine Learning

Contents and structure

Machine learning is an area within computer science where computer systems are designed to learn from large sets of examples. Machine Learning has recently gained great importance in domains such as natural language processing, image recognition, recommendation systems, the design of search engines, robots, and sensor systems, for extracting information from large scientific data sets. The focus of this course is on emerging trends in applied machine learning. Application areas discussed in the course include topics from computer vision, robotics, energy, and computational medicine and biology.

There are four modules: deep learning; causal inference; optimization, and time-series.

Acknowledgment:

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

Learning Outcome

After the course the candidate should be able to:

Knowledge

• explain the mathematical methods underlying machine learning, as well as how these methods are applied in selected application domains.
• assess machine learning problems in order to select and apply suitable methods and software tools to solve them.
• formulate and approach a new machine learning problem setting in a creative, critical, and systematic way.

Skills

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

General competence:

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

Entry requirements

General admission criteria for the PhD programme.

Recommended previous knowledge

A solid background in linear algebra, statistics, and familiarity with machine learning and Python programming is recommended.

Teaching methods

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

Compulsory learning activities

Student activity expected during lectures/seminars, see teaching methods

Assessment

The course is graded pass/fail based on an assignment (project 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 (project report): All support material is permitted

Oral exam: No support material

More about examination support material