Fundamentals of machine learningLaajuus (5 op)

Osaamistavoitteet

The student: - is familiar with the machine learning engineer role of information systems in industry. - understands the features of machine learning to apply on real world problems. - understands the mathematical foundations behind the machine learning algorithms as well as the paradigms supervised and unsupervised learning. - is able to choose and tune the appropriate machine learning models on existing real life problems. - possesses skills in using the off-the-shelf machine learning tools. Designing timely and efficient algorithms in a range of real-world applications. - understanding of the strengths and weaknesses of many popular machine learning approaches. - is able to design and implement various machine learning algorithms in a range of real-world applications.

Sisältö

Main contents of the course: - Supervised learning, knn algorithm as an example - Unsupervised learning, k-means algorithm as an example - Quantitative variables/data, standard deviation, covariance, correlation - Linear Regression - Topic detection, regular expressions - Natural Language Processing - Sentiment Analysis

Arviointikriteerit, tyydyttävä (1)

The student: - can apply the skills learned during the studies in a limited manner - is able to recognize and understands theories and tools learned during the study

Arviointikriteerit, hyvä (3)

The student: - is able to solve given problems by applying the skills learned during the study - is able to analyze relevant theories, and can independently make decisions based on these

Arviointikriteerit, kiitettävä (5)

The student: - can create more advanced solutions by utilizing the skills learned during the study - assesses relevant theories and can independently apply these in practice