scholarly journals Deep Teaching: Materials for Teaching Machine and Deep Learning

2019 ◽  
Author(s):  
Christian Herta ◽  
Benjamin Voigt ◽  
Patrick Baumann ◽  
Klaus Strohmenger ◽  
Christoph Jansen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Best Practices ◽  
Computer Science ◽  
Teaching Machine ◽  
Teaching Material ◽  
Abstract Concepts ◽  
Teaching Materials ◽  
Entry Barrier ◽  
Self Study

Machine learning (ML) is considered to be hard because it is relatively complicated in comparison to other topics of computer science. The reason is that machine learning is based heavily on mathematics and abstract concepts. This results in an entry barrier for students: Most students want to avoid such difficult topics in elective courses or self-study. In the project Deep.Teaching we address these issues: We motivate by selected applications and support courses as well as self-study by giving practical exercises for different topics in machine learning. The teaching material, provided as jupyter notebooks, consists of theoretical and programming sections. For didactical reasons, we designed programming exercises such that the students have to deeply understand the concepts and principles before they can start to implement a solution. We provide all necessary boilerplate code such that the students can primarily focus on the educational objectives of the exercises. We used different ways to give feedback for self-study: obscured solutions for mathematical results, software tests with assert statements, and graphical illustrations of sample solutions. All of the material is published under a permissive license. Developing jupyter notebooks collaboratively for educational purposes poses some problems. We address these issues and provide solutions/best practices. 

scholarly journals Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning

2021 ◽  
Vol 11 (15) ◽  
pp. 6912
Author(s):  
Jiaxin Tang ◽  
Yang Chen ◽  
Guozhen She ◽  
Yang Xu ◽  
Kewei Sha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Computer Science ◽  
Citation Analysis ◽  
Case Studies ◽  
Google Scholar ◽  
Manual Annotation ◽  
University Ranking

Google Scholar has been a widely used platform for academic performance evaluation and citation analysis. The issue about the mis-configuration of author profiles may seriously damage the reliability of the data, and thus affect the accuracy of analysis. Therefore, it is important to detect the mis-configured author profiles. Dealing with this issue is challenging because the scale of the dataset is large and manual annotation is time-consuming and relatively subjective. In this paper, we first collect a dataset of Google Scholar’s author profiles in the field of computer science and compare the mis-configured author profiles with the reliable ones. Then, we propose an integrated model that utilizes machine learning and node embedding to automatically detect mis-configured author profiles. Additionally, we conduct two application case studies based on the data of Google Scholar, i.e., outstanding scholar searching and university ranking, to demonstrate how the improved dataset after filtering out the mis-configured author profiles will change the results. The two case studies validate the importance and meaningfulness of the detection of mis-configured author profiles.

PENERAPAN DATA MINING UNTUK PEMESANAN BAHAN MENGAJAR DENGAN MENGGUNAKAN ALGORITMA APRIORI

2018 ◽  
Vol 2 (2) ◽  
pp. 119-131
Author(s):  
Asaziduhu Gea
Keyword(s):  
Data Mining ◽  
Computer Science ◽  
Printing Press ◽  
Teaching Material ◽  
Apriori Algorithm ◽  
Teaching Materials ◽  
Science Faculty ◽  
Computer Science Faculty ◽  
The University ◽  
Printing Presses

Abstrak   Percetakan Wendy adalah salah satu percetakan yang bekerja sama dengan Universitas Santo Thomas yang khususnya pasa fakultas Ilmu komputer, percetakan wendy bertempat di jalan setia budi simpang kampus UNIKA. Pimpinan dan pegawai percetakan wendy terkadang mengalami kesulitan untuk mengetahui seberapa banyak bahan ajar yang yelah tercetak dalam satu transaksi, sehingga pimpinan percetakan wendy masih manual/spekulasi dalam pencatakan bahan ajar. Penerapan data mining untuk pemesanan pencatakan bahan ajar pada fakultas ilmu computer universitas santo Thomas dipercetakan wendy ini bisa menghasilkan rules/aturan asosiasi bisa dilihat dan dianalisis hasilnya, sehingga pimpinan percetakan bisa melihat seberapantingi frekuensi bahan ajar yang sering terjadi ditiap transaksi. Maka dari itu, dilakukan analisis dan pengujian, diharapkan bisa memberikan informasi mengenai pola transaksi bahan ajar yang sering muncul.sehingga bisa membantu pemilik dalam mengambil keputusan untuk melakukan pemesanan dan pencetakan bahan ajar.   Kata kunci : Data Mining, Percetakan Wendy, Algoritma Apriori, Asosiasi.   Abstract   Wendy's printing is one of the printing presses in collaboration with the University of Santo Thomas, especially in the field of computer science, the printing of Wendy is located on the faithful road of UNIKA campus intersection. Print leaders and employees sometimes have difficulty finding out how much teaching material has been printed in one transaction, so that the leader of Wendy's printing press is still manual / speculating in teaching material.The application of data mining for ordering the teaching of teaching materials at the computer science faculty of the university of Saint Thomas is designed to produce this rule can produce rules / rules of association can be seen and analyzed the results, so that the printing leader can see a frequency of teaching materials that often occur in each transaction.Therefore, analysis and testing is carried out, it is hoped that it can provide information about the transaction patterns of teaching materials that often appear. So that it can help the owner in making decisions to order and print teaching materials.   Keywords: Data Mining, Wendy Printing, Apriori Algorithm, Association.

scholarly journals Introduction to Data Science - An Overview

2021 ◽  
pp. 407-410
Author(s):  
Thangakumar Jeyaprakash ◽  
Padmaveni K
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computer Science ◽  
Data Science ◽  
Data Transformation ◽  
Vital Role ◽  
Research Field ◽  
Fundamental Theory ◽  
Science And Engineering

Data science plays a vital role in the research field of computer science and engineering which involves collection of data, transformation, processing, describing, and modelling. In this article, fundamental theory of Data Science, Machine learning and Deep Learning with the scope and opportunities has been discussed. This helps the researchers to get a clarity on data science and its importance.

PENGEMBANGAN BAHAN AJAR IPA SMP PADA TEMA ENERGI DALAM TUBUH MENGGUNAKAN METODE 4STMD

EDUSAINS ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 166-175
Author(s):  
Gia Juniar Nur Wahidah ◽  
Sjaeful Anwar
Keyword(s):  
Concept Maps ◽  
The Body ◽  
Instructional Materials ◽  
Teaching Material ◽  
Test Results ◽  
Teaching Materials ◽  
Eligibility Criteria ◽  
Material Development ◽  
Selection Step ◽  
Selection Of

Abstract This research aims to produce science teaching materials in junior level with Energy in The Body as the theme using Four Steps Teaching Material Development  (4STMD). The material is presented in an integrated way so that students can  think holistically and contextually. The method used in this study is Research and Development. In this R&D methods is used 4STMD. There are four steps done on the development of teaching materials, the selection step, structuring step, characterization, and didactic reduction. Selection step includes the selection of indicators in accordance with the demands of the curriculum which is then developed with the selection of concepts and values that are integrated with the concept of science. Structuring step includes make macro structures, concept maps, and multiple representations. Characterization's step includes preparation instruments, then  trial to students to identify difficult concepts. The last, didactic reduction was done by neglect and the annotations in the form of sketches.The test results readability aspect instructional materials lead to the conclusion that by determining the main idea, the legibility of teaching materials reached 67%, with moderate readability criteria. Test results of feasibility aspects based on the results of questionnaires to the 11 teachers lead to the conclusion that the overall, level of eligibility teaching materials reached 91% with the eligibility criteria well. Keywords: teaching materials; energy; 4STMD Abstrak Penelitian ini bertujuan untuk menghasilkan bahan ajar IPA SMP pada tema Energi dalam Tubuh menggunakan metode Four Steps Teaching Material Development (4STMD). Materi disajikan secara terpadu sehingga memacu siswa untuk berpikir secara holistik dan kontekstual. Metode penelitian yang digunakan pada penelitian ini adalah metode penelitian dan pengembangan. Dalam penelitian dan pengembangan yang ini, digunakan metode Four Steps Teaching Material Development (4STMD). Terdapat empat tahap yang dilakukan pada pengembangan bahan ajar, yakni tahap seleksi, strukturisasi, karakterisasi, dan reduksi didaktik. Tahap seleksi meliputi pemilihan indikator yang sesuai dengan tuntutan kurikulum yang kemudian dikembangkan dengan pemilihan konsep dan nilai yang diintegrasikan dengan konsep IPA. Tahap strukturisasi meliputi pembuatan struktur makro, peta konsep, dan multipel representasi dari materi. Tahap karakterisasi meliputi penyusunan instrumen karakterisasi, kemudian uji coba kepada siswa untuk mengidentifikasi konsep sulit. Tahap terakhir, yaitu reduksi didaktik konsep terhadap konsep sulit. Reduksi didaktik yang dilakukan berupa pengabaian dan penggunaan penjelasan berupa sketsa. Hasil uji aspek keterbacaan bahan ajar menghasilkan kesimpulan bahwa berdasarkan penentuan ide pokok, keterbacaan bahan ajar mencapai 67%, dengan kriteria keterbacaan tinggi. Hasil uji aspek kelayakan berdasarkan hasil angket terhadap 11 orang guru menghasilkan kesimpulan bahwa secara keseluruhan tingkat kelayakan bahan ajar mencapai 91% dengan kriteria kelayakan baik sekali. Kata Kunci: bahan ajar; energi; 4STMD  Permalink/DOI: http://dx.doi.org/10.15408/es.v8i2.2039  

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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