Artificial Intelligence: Courses

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AI in Practice: Preparing for AI

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This course, AI in Practice: Preparing for AI, is the 1st course of the online education program AI in Practice. The course gives you a kaleidoscope of examples of applications of AI in various organizations, outlines the state of the art in modern AI research, and provides practical tools for integrating AI into your own organization. The program AI in Practice is built from two initial courses, AI in Practice: Preparing for AI and AI in Practice: Applying AI. The AI in Practice: Preparing for AI course is designed for people who want to apply AI in their own practical situation. For the experienced manager who wants to know what AI can do for her own organization. For the data analyst or business consultant who wants to understand how AI can be applied in the business processes of the company for which they work. For the student who wants to understand how the results of AI research can be translated into practical applications.

Material Type: Full Course

Authors: Arie van Deursen, Hennie Huijgens

AI in Practice: Applying AI

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Learn about the implementation and practical aspects of Artificial Intelligence and how to write a plan for applying AI in your own organization in a step-by-step manner. This course is not about difficult algorithms and complex programming; it is a course for anyone interested in learning how to integrate AI into their own organization. To understand how current Artificial Intelligence applications can be successfully integrated in organizations, we look at different examples. For instance, how ING uses reinforcement learning for personalized dialog management with its customers or how Radboud UMC uses diagnostic image analysis to discover early stages of infectious diseases. As part of our two-course program ‘AI in Practice’, this course will guide you in the practical aspects of applying AI in your own organization. You will examine typical applications of AI in use already and learn from their experience. These include challenges of implementation, lifecycle aspects, as well as the maintenance and management of AI applications. The course presents a variety of case studies from actual situations in public organizations and private enterprises in the healthcare, financial, retail and telecommunications sectors. These include Radboud UMC, the Municipality of Amsterdam, ING, Ahold Delhaize and KPN. ‘AI in Practice – Applying AI’ gives you the ammunition to understand the practical aspects required for the implementation of a variety of AI applications in your organization.

Material Type: Full Course

Authors: Arie van Deursen, Bram van Ginneken, Floris Bex, Marleen Huysman, Sennay Ghebreab

Independent Study in Geospatial Intelligence

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This course provides an understanding of how geospatial perspectives and technologies support all stages of emergency management activities, from small scale emergency management efforts to large scale disaster/humanitarian efforts. This includes learning about commonly used and emerging geospatial tools. It also includes an exploration of advancements in data collection, processing and analysis capabilities, such as unmanned aerial systems, geospatial artificial intelligence, volunteered geographic information, social media, and many more.

Material Type: Full Course

Author: Todd Bacastow

Artificial Intelligence and Librarianship

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Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 95,000 words on the topic, with a 3-400 item bibliography.

Material Type: Reading, Textbook

Author: Martin Frické

Introduction to Data-Centric AI

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Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline. This is the first-ever course on DCAI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications.

Material Type: Activity/Lab, Full Course, Lecture, Textbook

Authors: Anish Athalye, Curtis G. Northcutt, Jonas Mueller