- 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.
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
Course way of working and time table
This study is conducted as online studies. Studying is done independently based on the materials provided in Moodle and based on other provided materials. To complete the study the course project work needs to be returned within the given schedule and based on set requirements for the course project work. Project work and it’s guidelines is announced in Moodle at the beginning of the course. The project work is putting together several topics covered during this course.
In addition to lecture materials and self-study materials, instructional videos are provided such that they contribute to reviewing and deepening the issues covered in the lecture materials. Instructional videos can also provide guidance on how to go through the topics and also provide the information needed to do project work.