Applied Machine Learning (5cr)

Materials

1. VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data (1 edition). Link (https://www.amazon.com/Python-Data-Science- Handbook-Essential/dp/1491912057/ref=sr_1_1?crid=24YS9Z1DKV60D&keywords=python+data+science+handbook&qid=1567435393&s=books& sprefix=python+data+%2Cstripbooks-intl-ship%2C244&sr=1-1) 2. Raschka, S., & Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition. Link (https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/178995575 0) 3. Scikit-learn: Machine learning in Python—Scikit-learn 0.21.3 documentation. (ei pvm.). Fetched in 2.9.2019 from https://scikit-learn.org/stable/index.html (https://scikit- learn.org/stable/index.html) 4. Brownlee, J. (2016, kesäkuuta 9). Your First Machine Learning Project in Python Step-By-Step. Fetched in 2.9.2019, from Machine Learning Mastery website: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ (https://machinelearningmastery.com/machine-learning-in-python-step-by-step/)

Evaluation scale

Approbatur - Laudatur

Content

1. Introduction to machine learning 2. Introducing Python 3. Reading and cleaning data and plotting 4. Preprocessing and feature extraction 5. Unsupervised ML for data exploration 6. Supervised machine learning 7. Evaluation and optimisation of the models

Objective

Students who complete this course successfully will be aware of the practical implementation and usage of machine learning algorithms. Furthermore, they will be able to apply machine learning algorithms in real problems using efficient programming languages, for example Python.

Methods of completion

Lectures by the course instructor 20 hours + exercises (programming) 20 hours. Total 40 hours