Project-Based Learning in a Machine Learning Course with Differentiated Industrial Projects for Various Computer Science Master Programs

2020 ◽  
Author(s):  
Raphael Brungel ◽  
Johannes Ruckert ◽  
Christoph M. Friedrich
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Project Based Learning ◽  
Industrial Projects

Project-based learning in a graduate course on home appliance engineering

2021 ◽  
pp. 002072092110134
Author(s):  
Yeon Kim ◽  
Suk Lee ◽  
Changsun Ahn
Keyword(s):  
Quantitative Analysis ◽  
Engineering Education ◽  
Graduate School ◽  
Creative Thinking ◽  
Thinking Skills ◽  
Project Based Learning ◽  
Learning Method ◽  
Graduate Course ◽  
Industrial Projects ◽  
Home Appliance

Project-based learning is one of the popular and promising approaches in engineering education. The current study reports on a curriculum that was designed and implemented by a graduate school to help students gain knowledge and creative thinking skills through collaboration between different majors during industrial projects in a graduate course on home appliance engineering. The students selected the topics, planned the project, conducted research, produced a prototype, and presented their results under the guidance of a group of advisors consisting of professors, technical advisors, and industry mentors. A quantitative analysis showed that this approach was effective in improving the students’ attitude toward engineering. Furthermore, a qualitative analysis showed that this learning method helped students learn how to communicate and present effectively, to flexibly approach projects, and to understand the practices of industrial research. Based on the findings, the current study discusses how the project-based learning helped students advance.

Creativity in CS1: A Literature Review

2022 ◽  
Vol 22 (2) ◽  
pp. 1-26
Author(s):  
Sadia Sharmin
Keyword(s):  
Science Education ◽  
Literature Review ◽  
Computer Science ◽  
Creative Thinking ◽  
Computer Science Education ◽  
Project Based Learning ◽  
Retention Rates ◽  
Introductory Programming ◽  
Introductory Programming Courses ◽  
Programming Courses

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

scholarly journals John Senior and Éva Gyarmathy: AI and Developing Human Intelligence, Future Learning and Educational Innovation

2021 ◽  
Vol 28 (3) ◽  
pp. 442-446
Author(s):  
Valentin Kuleto ◽  
Milena Ilić
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Problem Solving ◽  
Computer Science ◽  
Image Recognition ◽  
Language Processing ◽  
Educational Innovation ◽  
Human Intelligence ◽  
Intelligent Machines ◽  
Future Learning

AI is a branch of computer science that emphasises the development of intelligent machines that think and work like humans. Examples of AI applications are speech recognition, natural language processing, image recognition etc. The term ML represents the application of AI to enable systems’ ability to learn and improve based on experience, without the explicit need for programming, using various problem-solving algorithms. For example, in machine learning, computers learn based on the data they process, not program instructions

Guest Column

2021 ◽  
Vol 52 (2) ◽  
pp. 46-70
Author(s):  
A. Knop ◽  
S. Lovett ◽  
S. McGuire ◽  
W. Yuan
Keyword(s):  
Machine Learning ◽  
Approximation Algorithms ◽  
Computer Science ◽  
Data Structures ◽  
Coding Theory ◽  
Communication Complexity ◽  
Property Testing ◽  
Theoretical Computer Science ◽  
Streaming Algorithms ◽  
Theoretical Computer

Communication complexity studies the amount of communication necessary to compute a function whose value depends on information distributed among several entities. Yao [Yao79] initiated the study of communication complexity more than 40 years ago, and it has since become a central eld in theoretical computer science with many applications in diverse areas such as data structures, streaming algorithms, property testing, approximation algorithms, coding theory, and machine learning. The textbooks [KN06,RY20] provide excellent overviews of the theory and its applications.

Introduction and Implementation of Machine Learning Algorithms in R

2019 ◽  
pp. 126-147
Author(s):  
S. R. Mani Sekhar ◽  
G. M. Siddesh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Given

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

scholarly journals Reports on the 2017 AAAI Spring Symposium Series

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 99-106
Author(s):  
Jeannette Bohg ◽  
Xavier Boix ◽  
Nancy Chang ◽  
Elizabeth F. Churchill ◽  
Vivian Chu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
User Experience ◽  
Object Perception ◽  
Learning Systems ◽  
Language Understanding ◽  
Social Good ◽  
Symposium Series ◽  
The Social

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27–29, 2017 on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01); Computational Construction Grammar and Natural Language Understanding (SS-17-02); Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03); Designing the User Experience of Machine Learning Systems (SS-17-04); Interactive Multisensory Object Perception for Embodied Agents (SS-17-05); Learning from Observation of Humans (SS-17-06); Science of Intelligence: Computational Principles of Natural and Artificial Intelligence (SS-17-07); and Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place.

scholarly journals Economy-on-demand and the fairness of algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 3-16
Author(s):  
Claudia Schubert ◽  
Marc-Thorsten Hütt
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Legal Standards ◽  
On Demand ◽  
Legal Enforcement ◽  
Digital Agency

Algorithms are the key instrument for the economy-on-demand using platforms for its clients, workers and self-employed. An effective legal enforcement must not be limited to the control of the outcome of the algorithm but should also focus on the algorithm itself. This article assesses the present capacities of computer science to control and certify rule-based and data-centric (machine learning) algorithms. It discusses the legal instruments for the control of algorithms and their enforcement and institutional pre-conditions. It favours a digital agency that concentrates expertise and bureaucracy for the certification and official calibration of algorithms and promotes an international approach to the regulation of legal standards.

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