Faculty of Business and Management Sciences · Business Administration (English) · Undergraduate
Course Objective
This course aims to teach students machine learning (ML) algorithms, data modeling techniques, and applications. Students will analyze datasets using supervised and unsupervised learning methods, implement machine learning models using SPSS and Python, and interpret the results.
Course Content
This course covers machine learning algorithms, data analysis techniques, and modeling processes. Students will learn to build supervised and unsupervised learning models using SPSS and Python. Key machine learning techniques such as regression, classification, clustering, dimensionality reduction, neural networks, and natural language processing (NLP) will be explored. Additionally, the course will focus on model evaluation, big data applications in machine learning, and AI ethics.
Course Learning Outcomes
- Students will be able to understand the fundamental concepts and algorithms of machine learning and create suitable models for different data types.
- Students will be able to implement machine learning models using SPSS and Python and conduct data analysis processes.
- Students will be able to create classification and prediction models using supervised and unsupervised learning methods.
- Students will be able to develop machine learning solutions for various industries using big data and natural language processing techniques.
- Students will be able to evaluate machine learning models' performance and analyze error rates.