Machine Learning Part 2, 3 Credits

Producing good machine learning models using the algorithms discussed in Introduction to Machine Learning – Part I typically depends on proper usage and preprocessing of the data, as well as the ability to interpret and act on the results generated by the method. Thereto, you will learn in this course more about the practical side of applying the machine learning techniques to actual problems including feature extraction methods, to figure out which part of the data is relevant to your problem, the bias-variance dilemma, to figure out if you have enough data for your model, ensemble learning, for combining more than one machine learning technique, and other experience-based practical recommendations. Armed with this knowledge, you will have to solve a classification problem in competition with the rest of the class, getting a hands-on experience in using Machine Learning.

The course is intended for working professionals.

ECTS Credits

3 Credits

Level of education

Second cycle, has second-cycle course/s as entry requirements (A1F)

School

School of Science and Technology

When is the course offered?

Prerequisites: At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" from the Swedish Upper Secondary School. The applicant must also have Machine Learning Part 1, 3 credits.

Selection: Academic points

Course syllabus

Application code: V5354

Note: You can only apply to this course in the Swedish Admission Round at antagning.se

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