CS321- Machine Learning, Data Mining & Business Analytics
Bachelor of Business Administration
Core Course
CS321- Machine Learning, Data Mining & Business Analytics
Course Unit Code: CS321
Type Of Unit: Core
Level of Course Unit: Second cycle
Year of Study: Third year
Semester: A’ Semester (Fall)
Number of ECTS Credits: 7.5
Class Contact Hours: 36
Mode of Delivery
Face to Face
Prerequisites
None
Course Objectives
The objective of this course is to teach students about machine learning and data mining and how they can be applied in business. The students will get an overview of all.
Learning Outcomes
- Understand the types of machine learning: supervised, unsupervised, active learning, reinforcement learning.
- Understand the fundamental concepts of machine learning like knowledge representation and optimisation.
- Understand and being able to implement data mining algorithms like association rule mining and clustering.
- Know how to implement data mining and machine learning algorithms in Weka.
- Learn how to use machine learning algorithms using scikit-learn in Python, like support vector machines and ensembles of trees.
- Learn how to test algorithms using cross- validation and to assess their performance using the right metrics.
- Use machine learning and data mining to solve real world problems.
Course Content
Course Features
Planned learning activities and teaching methods
Lectures; in-class discussion and debates; in-class exercises; problem sets; team work; video case studies, team presentations, interactive online learning via Moodle (quizzes, assignments, forums)
Assessment methods and criteria
10% class participation
60% exam
30% group project
Language of Instruction
English
Work Placement(s)
Not applicable
Readings
Textbooks:
1. Peter Norvig, Stuart J. Russell, Artificial Intelligence: A modern approach (4th edition), 2020
2. Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal , Data Mining: Practical machine learning tools and techniques (4th edition), 2016
Optional textbook:
3. Trevor Hastie & Robert Tibshirani,The elements of Statistical Learning, Springer, 2009
Online sources:
https://web.stanford.edu/~hastie/ElemStatLearn/ http://aima.cs.berkeley.edu/
https://www.cs.waikato.ac.nz/ml/weka/book.html
https://scikit-learn.org/stable/tutorial/basic/tutorial.html
English (UK) 
Ελληνικά