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e-journal
In this journal platform, you can find the articles which published under the open license. The journal including the disciplines:
Biological Sciences
Medicine
Case Reports
Physical Sciences
Social Sciences and Education
Mathematics, Engineering, and Computer Science
- Subjects:
- Medicine, Chemistry, Computing, Mathematics and Statistics, Biology, and Physics
- Keywords:
- Science Periodicals Computer science Physics Mathematics Education Technology Social sciences Environmental sciences Engineering Biology Medicine Physical sciences
- Resource Type:
- e-journal
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e-journal
Estonian Academy Publishers issues seven open access scientific journals, peer-reviewed, indexed and abstracted in international review publications and databases. All journals have an international Editorial board. Publications of the Estonian Academy Publishers cover almost all important areas of contemporary science. Our journals are peer reviewed and recognized by the Estonian Research Council and the Estonian Committee of Science Compentence as journals meeting international scientific standards.
- Subjects:
- Environmental Sciences, Physics, Chemistry, Archeology, Mathematics and Statistics, Biology, and Foreign Language Learning
- Keywords:
- Science Periodicals Uralic languages Ecology Mathematics Archaeology Technology Chemistry Social sciences Europe -- Baltic Sea Region Engineering Humanities Geology History Physics
- Resource Type:
- e-journal
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Video
In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available.
- Course related:
- COMP4431 Artificial Intelligence
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Artificial intelligence
- Resource Type:
- Video
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Video
An example of using least squares to do data fitting. In this example, we demonstrate how to 1) fit a straight line using ordinary least-squares method, and 2) estimate value of a new input based on the fitted line.
- Course related:
- LSGI3242A Digital Terrain Modelling
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Least squares Regression analysis
- Resource Type:
- Video
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e-book
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Bayesian statistical decision theory Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets. If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Textbooks Statistics -- Computer programs
- Resource Type:
- e-book
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e-book
This book focuses on the discrete Fourier transform (DFT), discrete convolution, and, particularly, the fast algorithms to calculate them. These topics have been at the center of digital signal processing since its beginning, and new results in hardware, theory and applications continue to keep them important and exciting. This book uses an index map, a polynomial decomposition, an operator factorization, and a conversion to a filter to develop a very general and efficient description of fast algorithms to calculate the discrete Fourier transform (DFT). The work of Winograd is outlined, chapters by Selesnick, Pueschel, and Johnson are included, and computer programs are provided.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Fourier transformations Textbooks
- Resource Type:
- e-book
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Video
Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter.
- Course related:
- HTI34016 Introduction to Clinical Research
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Statistics Mathematical analysis Data mining Machine learning
- Resource Type:
- Video
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MOOC
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).
- Course related:
- LGT6801 Guided Study in Logistics I
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Big data Data mining Machine learning Mathematical statistics -- Data processing
- Resource Type:
- MOOC
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Video
Engineering Mathematics tutorial series covers aspects of applied mathematics including: multivariable calculus; vector field theory; differential equations; Laplace transforms and Fourier series.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Engineering mathematics
- Resource Type:
- Video
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