AI with PythonLaajuus (5 ECTS)

Objective

The key learning objectives of this course are to equip students with practical programming proficiency in Python, enabling efficient manipulation and analysis of complex data structures such as arrays, tables, vectors, and matrices. Students will gain foundational and applied knowledge in artificial intelligence, particularly in regression and classification methods, allowing them to accurately predict and classify data-driven outcomes. By the end of the course, students will be able to independently apply relevant AI techniques, evaluate and enhance model performance, and effectively integrate diverse AI tools to address real-world problems.

Content

After completing this course, the student will have strengthened their Python programming skills, enabling them to comfortably apply key language features, structures, and data handling techniques. They will confidently work with essential data structures such as arrays, tables, vectors, and matrices to effectively store, manipulate, and analyze datasets in various contexts.

Students will also have acquired foundational knowledge of artificial intelligence, including a clear understanding of its concepts, practical uses, and implications across diverse fields. They will possess practical skills in performing regression analyses, capable of applying both fundamental and advanced regression techniques to predict numerical outcomes accurately. Additionally, students will be proficient in classification methods, able to select appropriate algorithms, execute classification tasks, and refine their models for enhanced performance. Overall, students will gain versatile AI competencies, allowing them to integrate multiple AI concepts flexibly to address complex, real-world challenges.