Elements of AI

Elements of AI is a series of free online courses created by MinnaLearn and the University of Helsinki. We want to encourage as wide a range of people as possible to learn what AI is, what it can (and can't) do, and how to get started creating AI methods. These courses combine theory with practical exercises and can be completed at your own pace.
The course is designed to introduce students, teachers, entrepreneurs and policy makers worldwide to the fundamentals and applications of AI in order to promote the popularization and development of AI.
The Elements of AI course consists of six topics covering fundamental concepts of AI, machine learning, neural networks, natural language processing, ethics, and social implications. Students can learn the fundamentals and applications of AI through online videos, interactive exercises, and real-world examples. In addition, learners can interact and discuss with other students and instructors to deepen their understanding of the course content.
An important feature of this course is that it does not require any prior knowledge of programming or math, making it suitable for anyone interested in artificial intelligence. By taking Elements of AI, learners can build a foundational knowledge of AI and develop a solid foundation for further in-depth research and study of AI.
In addition, Elements of AI provides a wealth of learning resources and practical examples that enable learners to gain a more intuitive understanding of the real-world applications of AI technology and its impact on society and the economy. This learning approach not only improves learners' practical and problem-solving skills, but also lays a solid foundation for their future development in the field of AI.
Elements of AI course syllabus:

Basic Concepts of Artificial Intelligence: Introduces the definition, historical development, and basic classification of artificial intelligence to help learners build a holistic understanding of artificial intelligence.

Machine Learning: a detailed description of the basic principles and algorithms of machine learning, including supervised learning, unsupervised learning, etc., as well as their applications to real-world problems.

Neural Networks: explains the basic structure and working principles of neural networks, covering core concepts such as perceptrons and back propagation, laying the foundation for understanding deep learning.

Natural Language Processing: Introduces the basic techniques and applications of natural language processing, such as text analysis, machine translation, and intelligent Q&A.

Computer Vision: explores the basic methods and techniques in the field of computer vision, including image recognition, target detection, and more.

Ethics and Social Impacts: Discusses the ethical issues and social impacts of the development of AI technology and guides learners to think about how to balance social responsibility with the development of technology.