Yale School of Public Health’s Post

Successfully attended 7 days Faculty development programme applications of mathematics in multi disciplinary domains Mathematics, often hailed as the language of the universe, serves as a cornerstone for understanding and solving complex problems in fields ranging from natural sciences and engineering to social sciences and beyond. Its universal applicability and versatility make it an indispensable tool for interdisciplinary exploration and innovation. In this faculty development program, we set out to unravel the interconnected web of mathematics across multi-disciplinary domains, aiming to equip educators with insights and strategies to infuse mathematical thinking into their teaching and research endeavors. 1. Mathematical applications in Financial Management and Risk Management problems. 2. Applications of mathematics in Electrical Engineering. 3. Graph theory and its applications in various fields. 4. Real life applications of Calculus : Area of usages 5. Utilizing linear regression and Logistic regression in Machine Learning. 6. Applications of mathematics in Biological Modelling. 7. Applications of mathematics in Computer Network Security. #research #facultydevelopmentprogram #phd #phdresearch #mathematics #workshop #learninganddevelopment #hr #hrhiring #university #registrar #dean #math

The ability to utilize mathematical thinking in problem-solving is a fundamental goal in mathematics education, yet it remains an elusive objective. The ultimate aim of educating learners is to build their capacity to conduct mathematical investigations, understanding how to apply mathematical theories practically in solving real-life problems. The application of mathematical thinking is not limited to the realm of mathematics alone but extends to encompass other fields such as science, technology, and economics. Several governments have observed the correlation between economic progress and achieving financial well-being with the prevalence of what is known as "mathematical illiteracy." Consequently, eradicating such illiteracy has become a goal for many governments. Mathematical literacy is defined as follows: "It is the ability to use mathematics in solving daily life problems and various professional challenges." In another definition: "It is the ability to think mathematically, to prove, model, and establish connections between various ideas."

Check out our latest publication with Alaa Alslaity, Ph.D. and Rita Orji, PhD This paper presents a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: (1) increasing interest in this domain, (2) supervised machine learning are the most popular algorithms, (3) text datasets in the English language are the most common data source, and (4) most research use Accuracy to evaluate performance.  Based on the findings, we suggest future directions and recommendations for developing human-centered systems. You can read the full paper here: https://lnkd.in/eFXSYQCe Dalhousie University, Dalhousie Faculty of Computer Science

Applied mathematics is the application of mathematical methods by different fields such as physics, engineering, medicine, biology, finance, business, computer science, and industry. Thus, applied mathematics is a combination of mathematical science and specialized knowledge. The term "applied mathematics" also describes the professional specialty in which mathematicians work on practical problems by formulating and studying mathematical models. For Space Technology,you must be a Excellent candidate in mathematics feild.

"Mathematics Education in the Age of Artificial Intelligence: How Artificial Intelligence can Serve Mathematical Human Learning (Mathematics Education in the Digital Era Book 17) (English Edition)", de Philippe R. Richard, Mª Pilar Vélez, Steven Van Vaerenbergh . "As mathematicians we are interested in what AI can do for mathematics, and as mathematics educators we are interested in what it can do for mathematics education."

Continuing our journey through the Mathematics Department, let's delve into the Mathematics major. Department Overview: The Department of Mathematics was established by a group of distinguished academics to provide comprehensive mathematical education, aiming to produce outstanding graduates in scientific and research fields. The department's curriculum includes pure and applied mathematics, as well as applications in computing. Department Objectives - Develop students' skills in analysis, research, and inference - Foster creative thinking and systematic problem-solving - Disseminate scientific knowledge in the community and encourage students to innovate   and conduct research Fields of Study - Pure Mathematics: Includes number theory, mathematical logic, algebra, and various types    of analysis (numerical, real, and functional). - Applied Mathematics: Covers theoretical physics (such as quantum theory and cosmology),   data science, and statistics. - Mathematics Applications in Computing: Encompasses the study of algorithms,   cryptography, and mathematical modeling. Career Fields 1. Scientific Research:  Positions in universities and specialized research centers. 2. Space Agencies and Atomic Energy Authorities:  Utilizing mathematics for design and data analysis. 3. Statistical and Data Analysis Centers:  Providing accurate analyses and forecasts in various fields. 4. Higher Education:  Teaching mathematics in schools and universities. 5. Industry and Technology:  Solving complex problems, developing software, and predictive analytics. 6. Financial Sector:  Financial analysis and risk assessment in banks and insurance companies. Stay tuned for more updates on our exciting programs and opportunities! 🚀 #FSCU #Sci_Techtalent #Mathematics #Research #Education #DataScience #AppliedMathematics #PureMathematics #Computing #CareerOpportunities

計算論的思考と自己調整学習 🔓 Pasterk, S., & Benke, G. (2024). Computational thinking for self-regulated learning. In ITiCSE 2024: Proceedings of the 2024 on Innovation and Technology in Computer Science Education (Vol. 1, pp. 640-645). ACM. https://lnkd.in/g-fMZVSX

Mathematics is beautiful!🥰 This is a mantra I often share with my students, and I strive to show them this beauty by making math fun and engaging. 😹🎉😸 In a recent class on the Fourier series at Miva Open University, I used my favourite graphing tool, GeoGebra, to demonstrate the graphical representation of the Fourier series for the function 𝑓(𝑥)=𝑥² over the interval (0, 𝜋) with a period of 2𝜋.📈 Witnessing the elegance of these graphs not only deepened my students' understanding of the analytic solutions but also sparked their curiosity to explore the real-life applications of the Fourier series. As I've emphasized in my recent posts, leveraging technology in education is essential. It transforms the seemingly impossible into achievable goals and broadens our horizons. I am continuously exploring new and exciting tools to simplify mathematics and data science teaching. Please share other amazing graphing tools in the comments if you know of any. Your suggestions are highly appreciated! Thank you! #MivaOpenUniversity #GeoGebra #FourierSeries #Mathematics #DataScience #Technology

? AJBT 1. RANDOM WALKS AND MATHEMATICAL DISCOVERY (SCI 199Y, L0412, 1997-98) Wednesdays, 2:00 - 4:00 p.m., OISE room C-155 Random walks are a fun, exciting, and intriguing topic in probability theory. The simplest random walk involves repeatedly making $1 bets, and asking such questions as: Will you eventually go broke? What is the probability that you will get rich first? What is the probability that you can keep playing forever? It also considers philosophical questions such as, what is the difference between "having probability 0" and "impossible"? This course will use random walks as a backdrop to examining a variety of issues in the learning of new mathematics, such as: How do people learn mathematics? Why do some learn faster than others? What is "math anxiety"? How is mathematics best taught? Are alternative teaching methods better than standard lectures? Do issues of gender and race come into play? How do mathematical geniuses think about mathematics? To succeed in this course, it is NOT necessary to be good at mathematics. Rather, it is important to be able to enthusiastically discuss and analyze mathematical thinking and learning. Professor Jeffrey S. Rosenthal, Department of Statistics, University of Toronto

Education Seminar today! https://lnkd.in/eRQn5_-U Location: Torrington Place (1-19), Room 102 Speaker: Prof Michael Grove, University of Birmingham Title: Approaches to feedback in the mathematical sciences: just what do students really think?  Abstract: Within the mathematical sciences there exist particular challenges associated with the provision of timely and detailed feedback, both of which are important given the widespread use of formative, and typically weekly, problem sheet assessments to aid and structure the mathematical development of learners. In this talk I will report on the outcomes from a cycle of action research that was designed to enhance the feedback received by students and their subsequent engagement with it in a large research-intensive mathematical sciences department along with more recent work to explore how students engage with additional opportunities for support and feedback to aid their mathematical learning. Student views on the current feedback they receive will be discussed, but more broadly the findings offer insight into alternative feedback practices that mathematical sciences departments might wish to explore.