AI Essentials

This introductory online course provides a comprehensive overview of Artificial Intelligence (AI). You will explore the impact of AI on our lives and economy, gain an understanding of its definition, historical development, and future potential. The course covers essential concepts such as algorithms, machine learning, neural networks, and deep learning, highlighting their interconnections. It delves into potential AI applications in various fields, discussing benefits, risks, and limitations, along with current regulations and debates. You will gain insight into machine learning through supervised, unsupervised, and reinforcement learning, including deep learning with convolutional neural networks. Finally, you will learn how algorithms enable machines to learn.

By the course's end, you will have a solid understanding of AI fundamentals and its industry impact. This beginner-friendly course caters to professionals and individuals seeking to comprehend the basics of AI, its capabilities, and how machines learn, with or without prior knowledge in the field.

🎯 Upon completion of the course, students will be able to define AI, recognize its types and origins, and differentiate the basic concepts of machine learning, neural networks and deep learning. Furthermore, they will describe how AI capabilities can be used in various fields, and identify its benefits, risks, limitations and regulation issues. Finally, students will be able to explain how machines learn and identify how algorithms work in order to achieve this.

📌 In particular, upon completion learners should…

• Explain what AI and machine learning is, including its differences with neural networks and deep learning.
• Recognise the AI capabilities in use cases, along with its benefits, risks, limitations and regulation issues.
• Identify the different types of machine learning and give a high level explanation of how a machine learns.
• Identify some of the most used algorithms in each of the machine learning types.

• Study Time (in h): 10-12 hours
• Target audience: Professionals and students with no or basic knowledge of AI who want to have an overview of how AI works along with its main concepts.
• Is this offering for me? This course targets professionals, students, entrepreneurs or free-lancers interested in getting started with AI, looking to understand concepts such as Artificial Intelligence, Machine Learning and Deep Learning and looking for answers about what AI is capable of doing nowadays, along with the AI limitations, risks and regulations. No pre-knowledge of AI is required.
• Type of offering: This is a self-paced online course, you can stop the course and continue with your learning at your own times. The offering comprises a video-based course along with interactive activities, and quizzes for testing your own knowledge.

What is AI

In this module you will learn what artificial intelligence is from a holistic perspective. From how the field has been defined to its impact on the economy and society. You will also explore the history of AI, its current state, and its potential for the future. Additionally, you will discover the basics of machine learning, including the concepts of algorithms, neural networks and deep learning.-

• Defining AI
• History of AI
• The basics of ML
• The difference between AI and ML

What can ML do

In this module you will learn which capabilities AI adds to machines, its potential applications in different fields, benefits, risks, limitations, and challenges associated with its use. You will also learn the basics about the current regulatory landscape around AI and ongoing debates about its use. By the end of the module, you will have a deeper understanding of the potential of AI and its impact on various industries.-

• The AI capabilities
• Fields of application
• The benefits of AI
• Limitations and risks of AI
• The regulation of AI

How do machines learn

In this module you will learn some basic concepts such as AI model, generalisation, overfitting and underfitting. The central part of the module concentrates on how we make machines learn, going through an explanation of each of the three ML Learning types: supervised, unsupervised and reinforcement learning, along with a clear definition. Moreover, you will see what problems can be solved with each of the learning types and the advantages and disadvantages of using these approaches. Finally, you will discover what neural networks and deep learning is and how we use them in order to make machines learn.

• ML models
• Generalization, underfitting and overfitting
• Introduction to the ML Types
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

How do algorithms work

In this module, you will be able to relate the machine learning types with their problems and algorithms. You will get through a series of high-level explanations of algorithms: linear regression, KNN, K-means, pseudo-labelling method and Q-learning, with the help of an example. Finally, you will get to know how deep learning works from the perspective of teaching a machine applying supervised learning and a Convolutional Neural Network algorithm.

• Linear and non-linear regression
• KNN
• K-means clustering
• Pseudo-labelling method
• Co-training
• Q-learning
• Convolutional Neural Networks (CNN)

Are you ready to embark on an extraordinary learning journey? We invite you to be a part of our course!

Our content, available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), can be freely shared, adapted, and used for commercial purposes with proper attribution to "AppliedAI Institute for Europe gGmbH." We offer SCORM and Articulate Storyline files for easy integration into Learning Management Systems (LMS) or educational websites/personal projects. Articulate files are fully editable, allowing you to customize content as needed. Additionally, you also have access to all course MP4 video files.