DOCSLIB.ORG

Exploring the Use of Artificial Intelligent Systems in STEM Classrooms Emmanuel Anthony Kornyo Submitted in Partial Fulfillment

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Exploring the Use of Artificial Intelligent Systems in STEM Classrooms Emmanuel Anthony Kornyo Submitted in Partial Fulfillment Exploring the use of Artificial Intelligent Systems in STEM Classrooms Emmanuel Anthony Kornyo Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2021 © 2021 Emmanuel Anthony Kornyo All Rights Reserved Abstract Exploring the use of Artificial Intelligent Systems in STEM Classrooms Emmanuel Anthony Kornyo Human beings by nature have a predisposition towards learning and the exploration of the natural world. We are intrinsically intellectual and social beings knitted with adaptive cognitive architectures. As Foot (2014) succinctly sums it up: “humans act collectively, learn by doing, and communicate in and via their actions” and they “… make, employ, and adapt tools of all kinds to learn and communicate” and “community is central to the process of making and interpreting meaning—and thus to all forms of learning, communicating, and acting” (p.3). Education remains pivotal in the transmission of social values including language, knowledge, science, technology, and an avalanche of others. Indeed, Science, Technology, Engineering, and Mathematics (STEM) have been significant to the advancement of social cultures transcending every epoch to contemporary times. As Jasanoff (2004) poignantly observed, “the ways in which we know and represent the world (both nature and society) are inseparable from the ways in which we choose to live in it. […] Scientific knowledge [..] both embeds and is embedded in social practices, identities, norms, conventions, discourses, instruments, and institutions” (p.2-3). In essence, science remains both a tacit and an explicit cultural activity through which human beings explore their own world, discover nature, create knowledge and technology towards their progress and existence. This has been possible through the interaction and applications of artifacts, tools, and technologies within the purviews of their environments. The applications of technologies are found across almost every luster of organizational learning especially teacher education, STEM, architecture, manufacturing, and a flurry of others. Thus, human evolution and development are inexplicably linked with education either formally or informally. The 21st century has however seen a surge in the use of artificial intelligence (AI) and digital technologies in education. The proliferation of artificial intelligence and associated technologies are creating new overtures of digital multiculturalism with distinct worldviews of significance to education. For example, learners are demonstrating digital literacy skills and are knowledgeable about AI technologies across every specter of their lives (Bennett et al., 2008). It is also opening new artesian well-springs of educational opportunities and pedagogical applications. This includes mapping new methodological pathways, content creation and curriculum design, career preparations and indeed a seemingly new paradigm shift in teaching STEM. There is growing scholarly evidence about the use and diffusion of these technologies in K-12 and higher education (Bonk & Graham, 2012; Hew & Brush, 2007; Langer, 2018; Mishra & Koehler, 2006). Some of these include the Sphero robots, Micro Bit, Jill Watson, BrickPi3 Classroom kit, Engino STEM Mechanic, Lego Education WeDo Core Set and Spike. Both educators and learners are using these in STEM programs as well as other education related activities. Just as human activities and interactions with artifacts and tools shaped and redefined the scientific-technological feat of previous generations, so the contemporary digital technological era seems to be on a similar trajectory. However, there is sparsity of empirical scholarship on the pedagogical prospects and effectiveness of artificial intelligence in STEM classrooms. Also, it should be noted that scholarship on how AI impacts pedagogical content knowledge of STEM educators and how learners perceive these technologies are just emerging. In addition, the recent COVID-19 pandemic (Ghandhi et al., 2020; Rasmussen et al., 2020) has unexpectedly created a renewed synergy towards the applications of digital technologies in teaching STEM. In the context of this force majeure (COVID-19), the traditional brick and mortar educational spaces metamorphosed into digital spaces with the applications of many artificial intelligent technologies and resources in the arena of education. This doctoral dissertation study examined these enigmas including how educators use these technologies in STEM classrooms. The study is informed by activity theory or cultural-historical activity theory (Engeström et al., 2007; Hasan et al., 2014; Krinski & Barker, 2009; Oers, 2010; Vygotsky,1987). The study participants will be selected from educators currently integrating artificial intelligent systems and digital technologies in their respective STEM classrooms. Pre-data survey inquiry has shown that many educators were incorporating some forms of AIS into their STEM classrooms. In view of these, I have explored Sphero educational robots to interrogate the research topic. The Sphero Edu described as a “…STEAM-based toolset that weaves hardware, software, and community engagement to promote 21st century skills. While these skills are absolutely crucial, our edu program goes beyond code by nurturing students’ creativity and ingenuity like no other education program can” (Sphero, April 2020). The Sphero robots also have features and applications for designing and teaching STEM topics such as nature, space science, geometry, and other activities of pedagogical significance. Users could also design and write advanced engineering programs in JavaScript during STEM educational activities formally and outside of the classrooms. In essence, educators and students can learn designing, programming, engineering, mathematics, computational thinking, and hands-on skills reflective of the 21st century. In brief, the dissertation study research has explored artificial intelligence and emerging technologies and how these could transform and advance teaching and learning of STEM hence the research topic: Exploring the use of Artificial Intelligent Systems in STEM Classrooms. Methodologically, this is a qualitative study through the theoretical frameworks of activity theory as applicable to STEM education. The main research questions are: 1) Given that artificial intelligent systems and digital technologies have been applied in STEM educational domains (content, pedagogy, student learning, assessment). How does the application of AIS and digital technologies impact pedagogy in STEM educational activities? 2) Given that digital technology is transforming contemporary society in every facet. How/What does AIS tell us about how digital technology impacts STEM pedagogy? Data was collected from the study participants, archival sources, and others for analyses. It is hoped that the findings will inform and address theories of learning and teaching, policy and praxis in science education, teacher preparatory and professional development programs as it relates to STEM classrooms. Table of Contents List of Figures ................................................................................................................................ iv Acknowledgments .......................................................................................................................... v Dedication ..................................................................................................................................... vii Introduction ..................................................................................................................................... 1 Chapter 1: Literature Review ......................................................................................................... 6 1.1 Introductory Comments ......................................................................................................... 6 1.2 An Expose on Artificial Intelligent Systems and Digital Technology .................................. 8 1.3 What then is Artificial Intelligence? .................................................................................... 10 1.4 Digital Technology .............................................................................................................. 17 1.5 Some Characteristics of Digital Technology ....................................................................... 23 1.6 Preliminary Conclusion ....................................................................................................... 28 1.7 Digital Worldview and STEM Education ............................................................................ 29 1.8 Some Challenges .................................................................................................................. 46 1.9 Some Perspectives ............................................................................................................... 49 Chapter 2: Conceptual/Theoretical Framework ............................................................................ 52 2.1 Activity Theory: An Introductory Comment ....................................................................... 52 2.2 Activity Theory: A Description ........................................................................................... 55 2.3 Summary and Perspectives .................................................................................................
Load more

Recommended publications
  • Hosted By: Science and Mathematics Education Conference Castel, St. Patrick's Campus, Dublin City University 16Th – 17Th
    Hosted by: Science and Mathematics Education Conference CASTeL, St. Patrick’s Campus, Dublin City University 16th – 17th June 2016 Acknowledgements The SMEC2016 organising committee gratefully acknowledges support from the Centre of the Advancement of STEM Teaching and Learning (CASTeL), the Institute of Physics in Ireland (IOPI), the Royal Society of Chemistry (RSC) and the Sustainable Energy Authority of Ireland (SEAI). ISBN: 978-1-873769-62-1 Address for correspondence: Centre for the Advancement of STEM Teaching and Learning (CASTeL), Faculty of Science and Health, Dublin City University, Dublin 9 Ireland E: [email protected] ii WELCOME ADDRESS SMEC 2016 took place in Dublin City University, St. Patrick’s Campus on the 16th and 17th June 2016. With a title of STEM Teacher Education - Initial and Continuing professional development, this conference focussed on teacher education in STEM with papers presented in the areas of: Initial teacher education; including professional knowledge of teachers; teaching and learning in initial teacher education; relating theory to practice; and issues related to teacher education programs, policy and reform; In-service education; including in-service education and training; curricular reform and new programmes Continuous professional development for all teachers; including teachers as lifelong learners; methods and innovation in professional development; evaluation of professional development practices; and reflective practice, teachers as researchers, and action research. SMEC 2016 was the seventh in a series of biennial international Science and Mathematics Education Conferences to be hosted by CASTeL – the Centre for the Advancement of STEM Teaching and Learning. The purpose of this conference series is to provide an international platform for teachers and educators to discuss practices and share their experiences in the teaching and learning of mathematics and science.
    [Show full text]
  • Good for Enterprise – Android V1.9.1 Release Notes (Service Release)
    Good for Enterprise – Android v1.9.1 Release Notes (Service Release) Updated: April 27, 2012 Good for Enterprise – Android v1.9.1 (Service Release) .............................................................................................. 1 Issues Resolved ........................................................................................................................................................ 1 Good for Enterprise – Android v1.9.0 ........................................................................................................................... 1 New Features ............................................................................................................................................................ 1 Issues Resolved ........................................................................................................................................................ 2 Good for Enterprise – Android v1.8.1 (Service Release) .............................................................................................. 2 Good for Enterprise – Android v1.8.0 ........................................................................................................................... 3 New Features ............................................................................................................................................................ 3 Good for Enterprise – Android v1.7.5 Service Release ................................................................................................ 3
    [Show full text]
  • Full Academic Cv: Grigori Fursin, Phd
    FULL ACADEMIC CV: GRIGORI FURSIN, PHD Current position VP of MLOps at OctoML.ai (USA) Languages English (British citizen); French (spoken, intermediate); Russian (native) Address Paris region, France Education PhD in computer science with the ORS award from the University of Edinburgh (2004) Website cKnowledge.io/@gfursin LinkedIn linkedin.com/in/grigorifursin Publications scholar.google.com/citations?user=IwcnpkwAAAAJ (H‐index: 25) Personal e‐mail [email protected] I am a computer scientist, engineer, educator and business executive with an interdisciplinary background in computer engineering, machine learning, physics and electronics. I am passionate about designing efficient systems in terms of speed, energy, accuracy and various costs, bringing deep tech to the real world, teaching, enabling reproducible research and sup‐ porting open science. MY ACADEMIC RESEARCH (TENURED RESEARCH SCIENTIST AT INRIA WITH PHD IN CS FROM THE UNIVERSITY OF EDINBURGH) • I was among the first researchers to combine machine learning, autotuning and knowledge sharing to automate and accelerate the development of efficient software and hardware by several orders of magnitudeGoogle ( scholar); • developed open‐source tools and started educational initiatives (ACM, Raspberry Pi foundation) to bring this research to the real world (see use cases); • prepared and tought M.S. course at Paris‐Saclay University on using ML to co‐design efficient software and hardare (self‐optimizing computing systems); • gave 100+ invited research talks; • honored to receive the
    [Show full text]
  • Delivering a Machine Learning Course on HPC Resources
    Delivering a machine learning course on HPC resources Stefano Bagnasco, Federica Legger, Sara Vallero This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement LHCBIGDATA No 799062 The course ● Title: Big Data Science and Machine Learning ● Graduate Program in Physics at University of Torino ● Academic year 2018-2019: ○ Starts in 2 weeks ○ 2 CFU, 10 hours (theory+hands-on) ○ 7 registered students ● Academic year 2019-2020: ○ March 2020 ○ 4 CFU, 16 hours (theory+hands-on) ○ Already 2 registered students 2 The Program ● Introduction to big data science ○ The big data pipeline: state-of-the-art tools and technologies ● ML and DL methods: ○ supervised and unsupervised models, ○ neural networks ● Introduction to computer architecture and parallel computing patterns ○ Initiation to OpenMP and MPI (2019-2020) ● Parallelisation of ML algorithms on distributed resources ○ ML applications on distributed architectures ○ Beyond CPUs: GPUs, FPGAs (2019-2020) 3 The aim ● Applied ML course: ○ Many courses on advanced statistical methods available elsewhere ○ Focus on hands-on sessions ● Students will ○ Familiarise with: ■ ML methods and libraries ■ Analysis tools ■ Collaborative models ■ Container and cloud technologies ○ Learn how to ■ Optimise ML models ■ Tune distributed training ■ Work with available resources 4 Hands-on ● Python with Jupyter notebooks ● Prerequisites: some familiarity with numpy and pandas ● ML libraries ○ Day 2: MLlib ■ Gradient Boosting Trees GBT ■ Multilayer Perceptron Classifier MCP ○ Day 3: Keras ■ Sequential model ○ Day 4: bigDL ■ Sequential model ● Coming: ○ CUDA ○ MPI ○ OpenMP 5 ML Input Dataset for hands on ● Open HEP dataset @UCI, 7GB (.csv) ● Signal (heavy Higgs) + background ● 10M MC events (balanced, 50%:50%) ○ 21 low level features ■ pt’s, angles, MET, b-tag, … Signal ○ 7 high level features ■ Invariant masses (m(jj), m(jjj), …) Background: ttbar Baldi, Sadowski, and Whiteson.
    [Show full text]
  • DES Curriculum and Assessment Unit
    Primary & Secondary Education The background paper has very little reference to STEM education in the primary and post-primary sectors. Without a firm foundation in their STEM education at both primary and post-primary there will not be third or further education students interested in seeking to continue their STEM studies. There is a lot going on in the STEM area at present, particularly at post primary level for mathematics and the sciences, but review and revision is commencing in the primary sector. The SSTI (2006 – 2013) outlined how we must build a strong science foundation in both primary and second level education. The SSTI called for a strengthening of the links between primary and post- primary. These will continue to guide work on the STEM agenda for primary and post-primary. In addition, the links between post-primary and third level education must also be strengthened so that there is a bridge and not a chasm between the two sectors. There also must continue to be links nurtured between scientific institutions – both from the academic and business sectors – so as to motivate young people to realise the importance of STEM education for their futures and for the Irish economy. The work of SFI in this agenda is vital. Initiatives such as the annual Young Scientist and Technology exhibition and Scifest also play a key role in raising awareness of the STEM agenda and provide an opportunity for students to be creative, innovative and entrepreneurial. It also assists in promoting a positive attitude to careers in the STEM areas. The Department of Education and Skills is currently overseeing a significant amount of curricular reform in the STEM area.
    [Show full text]
  • SKA-Athena Synergy White Paper
    SKA-Athena Synergy White Paper SKA-Athena Synergy Team July 2018. Edited by: Francisco J. Carrera and Silvia Martínez-Núñez on behalf of the Athena Community Office. Revisions provided by: Judith Croston, Andrew C. Fabian, Robert Laing, Didier Barret, Robert Braun, Kirpal Nandra Authorship Authors Rossella Cassano (INAF-Istituto di Radioastronomia, Italy). • Rob Fender (University of Oxford, United Kingdom). • Chiara Ferrari (Observatoire de la Côte d’Azur, France). • Andrea Merloni (Max-Planck Institute for Extraterrestrial Physics, Germany). • Contributors Takuya Akahori (Kagoshima University, Japan). • Hiroki Akamatsu (SRON Netherlands Institute for Space Research, The Netherlands). • Yago Ascasibar (Universidad Autónoma de Madrid, Spain). • David Ballantyne (Georgia Institute of Technology, United States). • Gianfranco Brunetti (INAF-Istituto di Radioastronomia, Italy) and Maxim Markevitch (NASA-Goddard • Space Flight Center, United States). Judith Croston (The Open University, United Kingdom). • Imma Donnarumma (Agenzia Spaziale Italiana, Italy) and E. M. Rossi (Leiden Observatory, The • Netherlands). Robert Ferdman (University of East Anglia, United Kingdom) on behalf of the SKA Pulsar Science • Working Group. Luigina Feretti (INAF-Istituto di Radioastronomia, Italy) and Federica Govoni (INAF Osservatorio • Astronomico,Italy). Jan Forbrich (University of Hertfordshire, United Kingdom). • Giancarlo Ghirlanda (INAF-Osservatorio Astronomico di Brera and University Milano Bicocca, Italy). • Adriano Ingallinera (INAF-Osservatorio Astrofisico di Catania, Italy). • Andrei Mesinger (Scuola Normale Superiore, Italy). • Vanessa Moss and Elaine Sadler (Sydney Institute for Astronomy/CAASTRO and University of Sydney, • Australia). Fabrizio Nicastro (Osservatorio Astronomico di Roma,Italy), Edvige Corbelli (INAF-Osservatorio As- • trofisico di Arcetri, Italy) and Luigi Piro (INAF, Istituto di Astrofisica e Planetologia Spaziali, Italy). Paolo Padovani (European Southern Observatory, Germany). • Francesca Panessa (INAF/Istituto di Astrofisica e Planetologia Spaziali, Italy).
    [Show full text]
  • Download Free from the Itunes App Store Today!
    HHMI BULLETIN F ALL ’12 V OL .25 • N O . 03 • 4000 Jones Bridge Road Chevy Chase, Maryland 20815-6789 Hughes Medical Institute Howard www.hhmi.org Out of Africa • This beauty is Aedes aegypti, a mosquito that can transmit www.hhmi.org dengue and yellow fever viruses, among others. Originally from Africa, it is now found in tropical and subtropical regions around the world. While this mosquito prefers to feast on animals over humans, it lives in close proximity to blood-hungry relatives that will choose a human meal every time. HHMI investigator Leslie Vosshall and her lab group are studying the evolutionary changes behind the insects’ diverse dietary habits. Their research may help reduce mosquito-borne illnesses. Read about Vosshall and the arc of her scientific career in “Avant Garde Scientist,” page 18. perils of fatty liver vosshall on the scent über cameras vol. vol. 25 / no. / no. Leslie Vosshall 03 ObservatiOns tHe traininG PiPeLine to prepare and retain a topnotch, diverse scientific workforce, increasingly necessary if the market for biomedical researchers trainees should be introduced to multiple career paths, according to strengthens outside of the United States in coming years). a June report by the niH biomedical Workforce Working Group. One of those options—the staff scientist—could use a boost. Co-chaired Today, these scientists bring stability to many labs and provide by Princeton University President shirley tilghman (see “the Future important functions as part of institutional core facilities, but have a of science,” HHMI Bulletin, May 2011) and sally rockey, niH deputy wide variety of titles and employment conditions.
    [Show full text]
  • Educators' Perceptions of Integrated STEM: a Phenomenological Study
    Journal of STEM Teacher Education Volume 53 | Issue 1 Article 3 April 2018 Educators’ Perceptions of Integrated STEM: A Phenomenological Study Brian K. Sandall University of Nebraska at Omaha, [email protected] Darrel L. Sandall Florida Institute of Technology, [email protected] Abram L. J. Walton Florida Institute of Technology, [email protected] Follow this and additional works at: https://ir.library.illinoisstate.edu/jste Part of the Curriculum and Instruction Commons, Engineering Education Commons, Other Education Commons, Other Teacher Education and Professional Development Commons, Science and Mathematics Education Commons, and the Secondary Education and Teaching Commons Recommended Citation Sandall, Brian K.; Sandall, Darrel L.; and Walton, Abram L. J. (2018) "Educators’ Perceptions of Integrated STEM: A Phenomenological Study," Journal of STEM Teacher Education: Vol. 53 : Iss. 1 , Article 3. DOI: doi.org/10.30707/JSTE53.1Sandall Available at: https://ir.library.illinoisstate.edu/jste/vol53/iss1/3 This Article is brought to you for free and open access by ISU ReD: Research and eData. It has been accepted for inclusion in Journal of STEM Teacher Education by an authorized editor of ISU ReD: Research and eData. For more information, please contact [email protected]. Journal of STEM Teacher Education 2018, Vol. 53, No. 1, 27–42 Educators’ Perceptions of Integrated STEM: A Phenomenological Study Brian K. Sandall University of Nebraska at Omaha Darrel L. Sandall and Abram L. J. Walton Florida Institute of Technology ABSTRACT The study utilized a semistructured interview approach to identify phenomena that are related to integrated STEM education by addressing the question: What are the critical components of an integrated STEM definition and what critical factors are necessary for an integrated STEM definition’s implementation? Thirteen expert practitioners were identified and interviewed.
    [Show full text]
  • B.Sc Computer Science with Specialization in Artificial Intelligence & Machine Learning
    B.Sc Computer Science with Specialization in Artificial Intelligence & Machine Learning Curriculum & Syllabus (Based on Choice Based Credit System) Effective from the Academic year 2020-2021 PROGRAMME EDUCATIONAL OBJECTIVES (PEO) PEO 1 : Graduates will have solid basics in Mathematics, Programming, Machine Learning, Artificial Intelligence fundamentals and advancements to solve technical problems. PEO 2 : Graduates will have the capability to apply their knowledge and skills acquired to solve the issues in real world Artificial Intelligence and Machine learning areas and to develop feasible and reliable systems. PEO 3 : Graduates will have the potential to participate in life-long learning through the successful completion of advanced degrees, continuing education, certifications and/or other professional developments. PEO 4 : Graduates will have the ability to apply the gained knowledge to improve the society ensuring ethical and moral values. PEO 5 : Graduates will have exposure to emerging cutting edge technologies and excellent training in the field of Artificial Intelligence & Machine learning PROGRAMME OUTCOMES (PO) PO 1 : Develop knowledge in the field of AI & ML courses necessary to qualify for the degree. PO 2 : Acquire a rich basket of value added courses and soft skill courses instilling self-confidence and moral values. PO 3 : Develop problem solving, decision making and communication skills. PO 4 : Demonstrate social responsibility through Ethics and values and Environmental Studies related activities in the campus and in the society. PO 5 : Strengthen the critical thinking skills and develop professionalism with the state of art ICT facilities. PO 6 : Quality for higher education, government services, industry needs and start up units through continuous practice of preparatory examinations.
    [Show full text]
  • STEM Education in Rural Schools: Implications of Untapped Potential
    National Youth-At-Risk Journal Volume 3 Issue 1 Fall 2018 Article 2 December 2018 STEM Education in Rural Schools: Implications of Untapped Potential Rachel S. Harris Georgia Southern University Charles B. Hodges Georgia Southern University Follow this and additional works at: https://digitalcommons.georgiasouthern.edu/nyar Recommended Citation Harris, R. S., & Hodges, C. B. (2018). STEM Education in Rural Schools: Implications of Untapped Potential. National Youth-At-Risk Journal, 3(1). https://doi.org/10.20429/nyarj.2018.030102 This essay is brought to you for free and open access by the Journals at Digital Commons@Georgia Southern. It has been accepted for inclusion in National Youth-At-Risk Journal by an authorized administrator of Digital Commons@Georgia Southern. For more information, please contact [email protected]. STEM Education in Rural Schools: Implications of Untapped Potential Abstract A large number of students in American public schools attend rural schools. In this paper, the authors explore rural science, technology, engineering, and math (STEM) education and the issues associated with STEM education for students, teachers, and parents in rural communities. Characteristics of rural STEM education are examined to highlight unique considerations for this context. The authors conclude with the recommendation that more research is needed that specifically addresses rural STEM education. Keywords rural education, STEM education, rural STEM education, STEM funding, education funding Cover Page Footnote This study was supported in part by funds from the Office of the Vice esidentPr for Research & Economic Development and the Jack N. Averitt College of Graduate Studies at Georgia Southern University. This essay is available in National Youth-At-Risk Journal: https://digitalcommons.georgiasouthern.edu/nyar/vol3/ iss1/2 Harris and Hodges: STEM Education in Rural Schools STEM Education in Rural Schools: Implications of Untapped Potential Rachel S.
    [Show full text]
  • MMS Copy of Supported Devices
    Good For Enterprise ‐ Certified Devices Country Network Platform Device All All Android Asus Eee Pad Transformer Prime All All Android Cisco Cius All All Android Google Nexus One All All Android HTC Desire (globally unlocked) All All Android HTC Legend (globally unlocked) All All Android HTC One (Google Play Edition) All All Android Kindle Fire All All Android LG Optimus 2X (globally unlocked) All All Android Moto G (Global unlocked) All All Android Moto G (US unlocked) All All Android Motorola Milestone (globally unlocked) All All Android Motorola Milestone 2 (globally unlocked) All All Android Motorola Xoom 2 WiFi All All Android Motorola Xoom WiFi All All Android Nexus 10 All All Android Nexus 4 All All Android Nexus 5 All All Android Nexus 7 (2012, 2013, WiFi only) All All Android Samsung Galaxy Nexus (globally unlocked) All All Android Samsung Galaxy Note 8 (WiFi only) All All Android Samsung Galaxy Note 10.1 (Intl. WiFi only) All All Android Samsung Galaxy Note 10.1 (U.S WiFi only) All All Android Samsung Galaxy Note 10.1 (2014) (WiFi only) All All Android Samsung Galaxy S II All All Android Samsung Galaxy S III (globally unlocked) All All Android Samsung Galaxy S III mini All All Android Samsung Galaxy S4 (Google Play Edition) All All Android Samsung Galaxy S4 (GT‐I9500) All All Android Samsung Galaxy S4 (GT‐I9505) All All Android Samsung Galaxy Tab 2 7 (WiFi only) All All Android Samsung Galaxy Tab 3 (7‐inch) All All Android Samsung Galaxy Tab 3 (8‐inch) All All Android Samsung Galaxy Tab 10.1 (WiFi only) All All Android Samsung
    [Show full text]
  • Delivering a Machine Learning Course on HPC Resources
    EPJ Web of Conferences 245, 08016 (2020) https://doi.org/10.1051/epjconf/202024508016 CHEP 2019 Delivering a machine learning course on HPC resources Stefano Bagnasco1, Gabriele Gaetano Fronzé1, Federica Legger1;∗ Stefano Lusso1, and Sara Vallero1 1Istituto Nazionale di Fisica Nucleare, via Pietro Giuria 1, 10125 Torino, Italy Abstract. In recent years, proficiency in data science and machine learning (ML) became one of the most requested skills for jobs in both industry and academy. Machine learning algorithms typically require large sets of data to train the models and extensive usage of computing resources, both for training and inference. Especially for deep learning algorithms, training performances can be dramatically improved by exploiting Graphical Processing Units (GPUs). The needed skill set for a data scientist is therefore extremely broad, and ranges from knowledge of ML models to distributed programming on heterogeneous resources. While most of the available training resources focus on ML algorithms and tools such as TensorFlow, we designed a course for doctoral students where model training is tightly coupled with underlying technologies that can be used to dynamically provision resources. Throughout the course, students have access to a dedicated cluster of computing nodes on local premises. A set of libraries and helper functions is provided to execute a parallelized ML task by automatically deploying a Spark driver and several Spark execution nodes as Docker containers. Task scheduling is managed by an orchestration layer (Kubernetes). This solution automates the delivery of the software stack required by a typical ML workflow and enables scalability by allowing the execution of ML tasks, including training, over commodity (i.e.
    [Show full text]