Bachelor
2020/2021
Data Science and Machine Learning with Python
Type:
Elective course (Business Administration)
Area of studies:
Management
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
3 year, 2 module
Mode of studies:
distance learning
Language:
English
ECTS credits:
3
Contact hours:
2
Course Syllabus
Abstract
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
Learning Objectives
- The aim of the course is to introduce basic knowledge of the programming using Python and to develop skills of its implication to solve managerial tasks.
Expected Learning Outcomes
- To know: basic terms and concepts of Python language; basic terms and concepts of machine learning;
- To have practical skills of: using built-in Python libraries; developing machine learning algorithms in Python; solving various business tasks for processing large amounts of information;
Course Contents
- Introduction to Machine LearningWelcome. Introduction to Machine Learning. Python for Machine Learning. Supervised vs Unsupervised
- RegressionIntroduction to Regression. Simple Linear Regression. Model Evaluation in Regression Models. Evaluation Metrics in Regression Models. Multiple Linear Regression. Non-Linear Regression
- ClassificationIntroduction to Classification. K-Nearest Neighbours. Evaluation Metrics in Classification. Introduction to Decision Trees. Building Decision Trees. Intro to Logistic Regression. Logistic regression vs Linear regression. Logistic Regression Training. Support Vector Machine
- ClusteringIntro to Clustering. Intro to k-Means. More on k-Means. Intro to Hierarchical Clustering. More on Hierarchical Clustering. DBSCAN
- Recommender SystemsIntro to Recommender Systems. Content-based Recommender Systems. Collaborative Filtering
