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Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses

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Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses Patrick Glauner 1 Abstract be assumed that this competition will intensify even fur- ther in the coming years and decades. In order to justify As a result of the rapidly advancing digital trans- the added value of three to five-year degree programs to formation of teaching, universities have started to prospective students, universities must differentiate them- face major competition from Massive Open On- selves from MOOCs in some way or other. line Courses (MOOCs). Universities thus have to set themselves apart from MOOCs in order to In this paper, we show how we address this challenge at justify the added value of three to five-year de- Deggendorf Institute of Technology (DIT) in our AI courses. gree programs to prospective students. In this DIT is located in rural Bavaria, Germany and has a diverse paper, we show how we address this challenge at student body of different educational and cultural back- Deggendorf Institute of Technology in ML and AI. grounds. Concretely, we present two innovative courses We first share our best practices and present two (that focus on ML and include slightly broader AI topics concrete courses including their unique selling when needed): propositions: Computer Vision and Innovation • Computer Vision (Section3) Management for AI. We then demonstrate how • Innovation Management for AI (Section4) these courses contribute to Deggendorf Institute of Technology’s ability to differentiate itself from In addition to teaching theory, we put emphasis on real- MOOCs (and other universities). world problems, hands-on projects and taking advantage of hardware. Particularly the latter is usually not directly pos- sible in MOOCs. In this paper, we share our best practices. 1. Introduction We also show how our courses contribute to DIT’s ability to differentiate itself from MOOCs and other universities. Over the past decade, access to knowledge has fundamen- tally changed. This process began around 2011, when Stan- ford professors Andrew Ng, Sebastian Thrun and others 2. MOOCs Have Become Game Changers made their AI courses available to everyone through online In addition to courses on AI, a variety of other courses on courses (Ng & Widom, 2014). This type of course is of- almost any topic have emerged on various MOOC platforms ten referred to as a Massive Open Online Course (MOOC). over the past decade. Those courses enable learners to study Popular MOOC platforms include Coursera, Udacity, edX, high-quality content from renowned professors, remotely, Udemy and others. Until 2011, AI could generally only be at their own speed and at little or no cost. Furthermore, studied in a limited number of available university courses collaborations with renowned universities and industry part- or from books or papers. Furthermore, those resources were ners have emerged. Some MOOC platforms offer career mainly available in developed countries. As a consequence, arXiv:2107.04024v2 [cs.CY] 13 Aug 2021 coaching, too. Companies have also launched collaborative potential learners in emerging markets could not easily ac- programs with MOOC platforms to train their employees. cess respective resources. Due to MOOCs, the so-called “democratization of AI knowledge” has begun to fundamen- There are plenty of examples of professionals who have tally change the way we learn and has given rise to new AI found new, high-paying jobs in various industries within superpowers, such as China (Lee, 2018). a short period of time after completing hands-on MOOCs, for example in the news (Lohr, 2020) or on LinkedIn. This We argue in Section2 that MOOCs have now given many is particularly true for IT, a sector that has traditionally universities and professors serious competitors. It can also been open to lateral entrants and autodidacts. In recent 1Department of Applied Computer Science, Deggendorf Insti- years, MOOCs have therefore become steadily more estab- tute of Technology, Deggendorf, Germany. Correspondence to: lished. This trend has also been further consolidated during Patrick Glauner <[email protected]>. the COVID-19 pandemic, as millions of people around the globe have been undergoing retraining (Bylieva et al., 2020). Proceedings of the 2 nd Teaching in Machine Learning Workshop, PMLR, 2021. Copyright 2021 by the author(s). Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses In summary, universities will be facing the following chal- international competition, the content of a today’s university lenges in the coming years: CV course must meaningfully differentiate itself from those by offering unique selling propositions. Based on these prin- 1. In just a few years many very good high school grad- ciples, we have started to teach this novel course in 2020 uates could decide against the traditional completion at DIT. Note that there is a separate deep learning course of a university degree program (if they do not aim for taught by a different professor in our department. Most an academic career). They would then rather acquire students take both courses in parallel and have previously all necessary practical skills through MOOCs within a taken an introductory machine learning course. few months or perhaps a year. In parallel, they could also gain practical experience by working part-time 3.1. Content or founding startups. As a consequence, they could quickly get excellent jobs and outperform traditional We provide students with a broad and deep background university graduates on the jobs market. in CV. That is why we discuss both, traditional and mod- 2. Due to the demographic change (Magnus, 2012) and ern neural network-based CV methods. In practice, suc- the potential lack of qualified new students, many uni- cessful CV applications tend to combine both approaches versities in developed countries may become unable (O’Mahony et al., 2019), in particular when only a limited to maintain their current size. In view of the return number of training examples are available (Ahmed & Islam, on investment, politicians or administrators may thus 2020). Concretely, we discuss the following topics in the probably sooner or later start thinking about closing first half of the term: individual departments or even entire universities. 3. Many (non-computer science) degree programs have • Introduction: applications, computational models for so far only taught traditional content, with little or vision, perception and prior knowledge, levels of vi- no link to the digital transformation and automation sion, how humans see through AI. If this important content continues to go • Pixels and filters: digital cameras, image representa- unnoticed in education, those degree programs will tions, noise, filters, edge detection almost certainly train their students for unemployment. • Regions of images: segmentation, perceptual group- ing, Gestalt theory, segmentation approaches, image Universities must face up to these challenges, which also compression by learning clusters provide many opportunities, though. By addressing these • Feature detection: RANSAC, Hough transform, Harris challenges and also taking even more advantage of their corner detector assets, such as enabling students to collaborate physically • Object recognition: challenges, template matching, and using on-site facilities, universities could emerge even histograms, machine learning stronger from that competition. Most importantly, univer- • Convolutional neural networks: neural networks, loss sities must differentiate themselves from MOOCs. In the functions and optimization, backpropagation, convolu- following sections, we show how we address these chal- tions and pooling, hyperparameters, AutoML, efficient lenges by teaching cutting-edge real-world content and tak- training, selected architectures ing advantage of physical university infrastructure. We also • Image sequence processing: motion, tracking image actively promote our courses through social media, press sequences, temporal models, Kalman filter, correspon- releases and other channels in order to attract more prospec- dence problem, optical flow, recurrent neural networks tive students. In addition, our courses are open to students • Foundations of mobile robotics: robot motion, sensors, of other departments, including electrical engineering, me- probabilistic robotics, particle filters, SLAM chanical engineering, healthcare or business. This allows • Advanced topics: 3D vision, generative adversarial us to support them in learning the tools of the 21st century networks, self-supervised learning that they need in order to actively contribute to the digital transformation of their disciplines. In the second half of the term, students work in groups of 1 to 4 members on a CV project. 3. Computer Vision Course 3.2. Unique Selling Propositions Popular MOOC platforms offer a number of excellent This course differentiates itself from other CV courses, in courses1 on computer vision (CV). In order to survive in particular MOOCs, as follows: 1 These include, but are not limited to, the fol- 1. Most CV courses taught on MOOC platforms or at lowing courses: http://www.udacity.com/ course/computer-vision-nanodegree--nd891, universities only include smaller, isolated problems http://www.coursera.org/learn/ that can be implemented on almost any commercially computer-vision-basics. available computer or by using cloud services. This Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses course includes a larger real-world project in the sec- course more
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