Advanced Machine Learning Methods (5 cr)

Evaluation scale

H-5

Content scheduling

- Natural language processing machine learning models
- Advanced specialized machine learning algorithms
- Machine learning model optimization tools
- Error metrics and ML model performance evaluation
- Use of suitable tools (e.g., a high-level ML application programming interface, like scikit-learn and TensorFlow) for building solutions

Objective

- Knowledge and skills to understand beyond-basic contemporary machine learning (ML) models and methods, and to choose and apply them in a principled and sound way
- Abilities for understanding connections to, and dependencies between, model/method properties and timely topics (e.g., ethics, sustainability, explainability).
- Abilities to solve a computational problem via machine learning without using a high-level ML application programming interface

Content

- Theory and practice of the beyond-basic contemporary machine learning (ML) models and methods
- Use of suitable tools (e.g., an ML application programming interface enabling both high and low level expression) for building solutions

Location and time

Lapland University of Applied Sciences, Rantavitikka Campus, 13.1.2025 - 15.5.2025.

Teaching methods

Lectures, workshops, examples, exercises and self-supervised work.

Exam schedules

The course will be graded based on personal work and exercises.

Assessment criteria, satisfactory (1)

Grade 1: The student knows the theory on the considered ML models and methods. The student is able to solve beyond-basic contemporary ML problems, using the considered tools.

Assessment criteria, good (3)

Grade 3: The student understand the theory on the considered ML models and methods. The student is able to solve a variety of beyond-basic contemporary ML problems, using the considered tools, suitably.

Assessment criteria, excellent (5)

Grade 5: The student understand the theory on the considered ML models and methods. The student is able to solve a variety of beyond-basic contemporary ML problems, using the considered tools, most suitably.

Qualifications

Introduction to Machine Learning Methods