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Probability Theory and Mathematical Statistics in Tver State University
Project MetaMath outlines a probability theory and mathematical statistics course offered at Tver State University. The course is offered over two semesters for a total of 9 credits. It includes lectures, laboratory work, seminars, course projects each semester, and exams. The goal of the course is to present basic information about probability models that account for random factors. Upon completing the course, students should have mastered key probability and statistics concepts and techniques. The course also discusses modernizing elements like pre-testing students and incorporating online homework assignments.
OMSU vs. EU comparative curricula study
This document compares the Discrete Mathematics curricula and courses between OMSU (National Research Ogarev Mordovia State University) in Russia and TUT (Tampere University of Technology) in Finland. It analyzes the competencies, topics, and learning outcomes covered in the Discrete Mathematics courses based on three levels of difficulty. Overall, the OMSU course covers more topics like set theory, combinatorics, algebraic structures, and coding theory over a longer duration, while the TUT course focuses more on number theory over a shorter period. The document proposes increasing engineering applications and using an online learning system to help modernize the Discrete Mathematics courses.
A Course of Calculus for IT-Students
This document outlines a course of calculus for IT students at Lobachevsky State University of Nizhni Novgorod. The course is divided into 3 terms covering sequences, differential calculus, integral calculus, and series. Tests and exams are given throughout each term to assess student competency in mathematical thinking and problem solving. The course aims to develop skills in applying modern mathematical tools. Plans are discussed to modernize the course by adding an introductory section to address low student preparation, using online tools like METAMATH to support independent work, and testing key concepts to address educational problems.
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The document discusses the discrete mathematics curriculum at Saint-Petersburg Electrotechnical University. It provides an overview of which discrete math topics are covered in each year of study for different degree programs. It also compares course parameters like credits and hours between the university and TUT. Key modules covered in the second year Math Logic and Algorithm Theory course are outlined. Competencies addressed in the curriculum are mapped to SEFI levels, with additional competencies covered uniquely at the university. Suggested modifications to improve the curriculum structure are presented.
Probability Theory and Mathematical Statistics
This document provides information about a Probability Theory and Mathematical Statistics course taught at KNITU, Russia. It includes details about the course such as the number of students, preliminary courses required, distribution of working time, topics covered in lectures and workshops/laboratories. It also compares the methodology and topics studied in this course to a similar course taught at TUT, Finland. Key differences highlighted include the use of Matlab at TUT and more emphasis on practical work/tutorials versus lectures. Overall competencies covered are also summarized and compared between the two courses based on the SEFI framework.
Optimization Methods
This document compares the optimization methods courses between KNITU (Russia) and TUT (Finland).
The KNITU course is mandatory, has fewer credits (3 vs 5), and less time spent (108 student hours vs 138). Key topics are similar but KNITU spends less time on lectures (10 vs 28) and nonlinear optimization.
The main difference is KNITU has fewer lectures, almost half that of TUT. This could be addressed by using an online math platform like Math-Bridge to provide additional lecture material and practice problems. Mid-term tests on Math-Bridge could help evaluate knowledge gained from the extra online content.
Course - Discrete Mathematics
This document summarizes the course content and structure for Discrete Mathematics at the National Research Ogarev Mordovia State University. The course is divided into 4 modules covering set theory, graph theory, algebraic structures, and coding theory. Students take exams and write 3 essays throughout the semester to assess their understanding of each module. Pedagogical methods include lectures, practice problems, subgroup work, computer programming assignments, and a final exam to evaluate students on a 100 point scale.
SEFI comparative study: Course - Algebra and Geometry
The document describes a course in Algebra and Geometry for Informatics and Computer Science (ICS) and Programming Engineering (PE) majors. It analyzes the course content based on the SEFI framework and finds that the course covers most competencies in linear algebra and geometry at the core and level 1 levels. Some level 2 and 3 competencies are also covered. However, not all competencies are addressed as some assume knowledge from secondary school, others are covered in other courses, and some are not necessary for the ICS and PE profiles.
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Probability Theory and Mathematical Statistics in Tver State University
Project MetaMath outlines a probability theory and mathematical statistics course offered at Tver State University. The course is offered over two semesters for a total of 9 credits. It includes lectures, laboratory work, seminars, course projects each semester, and exams. The goal of the course is to present basic information about probability models that account for random factors. Upon completing the course, students should have mastered key probability and statistics concepts and techniques. The course also discusses modernizing elements like pre-testing students and incorporating online homework assignments.
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A Course of Calculus for IT-Students
This document outlines a course of calculus for IT students at Lobachevsky State University of Nizhni Novgorod. The course is divided into 3 terms covering sequences, differential calculus, integral calculus, and series. Tests and exams are given throughout each term to assess student competency in mathematical thinking and problem solving. The course aims to develop skills in applying modern mathematical tools. Plans are discussed to modernize the course by adding an introductory section to address low student preparation, using online tools like METAMATH to support independent work, and testing key concepts to address educational problems.
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The document discusses the discrete mathematics curriculum at Saint-Petersburg Electrotechnical University. It provides an overview of which discrete math topics are covered in each year of study for different degree programs. It also compares course parameters like credits and hours between the university and TUT. Key modules covered in the second year Math Logic and Algorithm Theory course are outlined. Competencies addressed in the curriculum are mapped to SEFI levels, with additional competencies covered uniquely at the university. Suggested modifications to improve the curriculum structure are presented.
Probability Theory and Mathematical Statistics
This document provides information about a Probability Theory and Mathematical Statistics course taught at KNITU, Russia. It includes details about the course such as the number of students, preliminary courses required, distribution of working time, topics covered in lectures and workshops/laboratories. It also compares the methodology and topics studied in this course to a similar course taught at TUT, Finland. Key differences highlighted include the use of Matlab at TUT and more emphasis on practical work/tutorials versus lectures. Overall competencies covered are also summarized and compared between the two courses based on the SEFI framework.
Optimization Methods
This document compares the optimization methods courses between KNITU (Russia) and TUT (Finland).
The KNITU course is mandatory, has fewer credits (3 vs 5), and less time spent (108 student hours vs 138). Key topics are similar but KNITU spends less time on lectures (10 vs 28) and nonlinear optimization.
The main difference is KNITU has fewer lectures, almost half that of TUT. This could be addressed by using an online math platform like Math-Bridge to provide additional lecture material and practice problems. Mid-term tests on Math-Bridge could help evaluate knowledge gained from the extra online content.
Course - Discrete Mathematics
This document summarizes the course content and structure for Discrete Mathematics at the National Research Ogarev Mordovia State University. The course is divided into 4 modules covering set theory, graph theory, algebraic structures, and coding theory. Students take exams and write 3 essays throughout the semester to assess their understanding of each module. Pedagogical methods include lectures, practice problems, subgroup work, computer programming assignments, and a final exam to evaluate students on a 100 point scale.
SEFI comparative study: Course - Algebra and Geometry
The document describes a course in Algebra and Geometry for Informatics and Computer Science (ICS) and Programming Engineering (PE) majors. It analyzes the course content based on the SEFI framework and finds that the course covers most competencies in linear algebra and geometry at the core and level 1 levels. Some level 2 and 3 competencies are also covered. However, not all competencies are addressed as some assume knowledge from secondary school, others are covered in other courses, and some are not necessary for the ICS and PE profiles.
Mathematical foundations of fuzzy systems
This document discusses the mathematical foundations of fuzzy systems, including:
- The curriculum covers theory of fuzzy sets, theory of possibility, crisp vs. fuzzy values, model tasks, and possibilistic optimization tasks over two semesters for a total of 324 hours.
- The theory of possibility introduced in 1978 uses axiomatic approach and possibility measures to define possibilistic space and possibilistic (fuzzy) variables characterized by possibility distributions.
- Model tasks and possibilistic optimization tasks are presented, where the coefficients can be crisp or possibilistic variables.
Probability Theory and Mathematical Statistics in Tver State University
Probability Theory and Mathematical Statistics in Tver State University
Calculus - St. Petersburg Electrotechnical University "LETI"
This document provides an overview of the calculus concepts covered in school and in various university courses at the Electrotechnical University “LETI” in Saint Petersburg, Russia. It outlines the key competencies developed in functions, sequences, series, logarithmic/exponential functions, rates of change, differentiation, integration, and other topics. The levels of mastery increase across the core courses in Calculus, Computing Mathematics, and some additional advanced topics covered in only two specialized groups.
Discrete Mathematics
1. The document outlines discrete mathematics competencies covered at different levels in the undergraduate curriculum at Saint-Petersburg Electrotechnical University.
2. Many competencies are covered in the discrete mathematics course in the first year, while others are covered in courses like mathematical logic and algorithm theory in later years.
3. LETI aims to develop additional competencies beyond the SEFI levels, such as skills in mathematical logic, graphs, algorithms, and finite state machines.
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This document discusses a computer tutorial on probability theory and mathematical statistics that was developed for a bachelor's degree program in computer science and engineering. It provides details on the course, including the typical number and gender of students, prerequisite courses, and time allocation. It also outlines the history of the degree program and standards from 1990 to 2014. The document describes the contents, structure, and development of the computer tutorial, and shows some screenshots of different learning management systems used to deliver the tutorial over time, including Lotus Learning Space, IBM Workplace Collaborative Learning, and Blackboard.
Optimization Methods
This document provides an overview of optimization methods. It discusses both single-variable and multi-variable optimization techniques, including necessary and sufficient conditions for local minima. Specific optimization methods covered include golden section search, dichotomous search, gradient descent, Newton's method, the simplex method for linear programming problems, and the method of Lagrange multipliers for constrained optimization problems. The document is intended to provide information about an optimization methods course, including preliminary courses, time distribution, and types of optimization techniques taught.
Math Education for STEM disciplines in the EU
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International Activities of the University in academic field
The document summarizes the international activities of Kazan National Research Technical University (KNRTU-KAI) in academic fields. It outlines several milestones in the university's international relations starting from the 1950s when it first hosted foreign students. It then discusses KNRTU-KAI's participation in international projects, associations, and TEMPUS programs. The document also provides details on international accreditation of academic programs, the new German-Russian Institute of Advanced Technologies, and KNRTU-KAI's approach to developing new curricula/modules based on the qualifications framework of the European Higher Education Area.
How to design a miniature train set that always loops back well? Two question...
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TSU
The document discusses the mathematics education program at Tver State University. It provides data on the number of students in various degree programs in 1977 and 1994. It then discusses the requirements for the mathematics and computer science minor program, including necessary courses, assessments, credits, and workload. Finally, it compares the university's program to the standards of the SEFI (Société Européenne pour la Formation des Ingénieurs) organization at different levels, noting some additional topics that are not covered in the SEFI standards.
LETI - Mr. Posov
The document summarizes courses in discrete mathematics and mathematical logic/theory of algorithms taught at Saint Petersburg Electrotechnical University. The courses cover topics like number theory, combinatorics, binary relations, propositional/predicate logic, formal languages, and graph algorithms. Assessment includes individual homework assignments, tests, short in-lecture assignments, and written examinations with non-standard problems testing understanding. An alternative examination option allows converting a good first test mark to a programming, problem-solving, or essay assignment.
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Mathematical foundations of fuzzy systems
This document discusses the mathematical foundations of fuzzy systems, including:
- The curriculum covers theory of fuzzy sets, theory of possibility, crisp vs. fuzzy values, model tasks, and possibilistic optimization tasks over two semesters for a total of 324 hours.
- The theory of possibility introduced in 1978 uses axiomatic approach and possibility measures to define possibilistic space and possibilistic (fuzzy) variables characterized by possibility distributions.
- Model tasks and possibilistic optimization tasks are presented, where the coefficients can be crisp or possibilistic variables.
Probability Theory and Mathematical Statistics in Tver State University
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Calculus - St. Petersburg Electrotechnical University "LETI"
This document provides an overview of the calculus concepts covered in school and in various university courses at the Electrotechnical University “LETI” in Saint Petersburg, Russia. It outlines the key competencies developed in functions, sequences, series, logarithmic/exponential functions, rates of change, differentiation, integration, and other topics. The levels of mastery increase across the core courses in Calculus, Computing Mathematics, and some additional advanced topics covered in only two specialized groups.
Discrete Mathematics
1. The document outlines discrete mathematics competencies covered at different levels in the undergraduate curriculum at Saint-Petersburg Electrotechnical University.
2. Many competencies are covered in the discrete mathematics course in the first year, while others are covered in courses like mathematical logic and algorithm theory in later years.
3. LETI aims to develop additional competencies beyond the SEFI levels, such as skills in mathematical logic, graphs, algorithms, and finite state machines.
Probability Theory and Mathematical Statistics
This document discusses a computer tutorial on probability theory and mathematical statistics that was developed for a bachelor's degree program in computer science and engineering. It provides details on the course, including the typical number and gender of students, prerequisite courses, and time allocation. It also outlines the history of the degree program and standards from 1990 to 2014. The document describes the contents, structure, and development of the computer tutorial, and shows some screenshots of different learning management systems used to deliver the tutorial over time, including Lotus Learning Space, IBM Workplace Collaborative Learning, and Blackboard.
Optimization Methods
This document provides an overview of optimization methods. It discusses both single-variable and multi-variable optimization techniques, including necessary and sufficient conditions for local minima. Specific optimization methods covered include golden section search, dichotomous search, gradient descent, Newton's method, the simplex method for linear programming problems, and the method of Lagrange multipliers for constrained optimization problems. The document is intended to provide information about an optimization methods course, including preliminary courses, time distribution, and types of optimization techniques taught.
Math Education for STEM disciplines in the EU
The document discusses math education reforms in the EU. It notes declining math skills among students and describes efforts across Europe to shift from a content-focused approach to developing mathematical competencies. Recommendations include changing curricula to emphasize real-world problem solving, improving teacher training, and leveraging technology as a teaching tool while maintaining the important role of educators. Overall, the document outlines the need for pedagogical reforms to address shortcomings identified by assessments like PISA and better prepare students for STEM careers.
International Activities of the University in academic field
The document summarizes the international activities of Kazan National Research Technical University (KNRTU-KAI) in academic fields. It outlines several milestones in the university's international relations starting from the 1950s when it first hosted foreign students. It then discusses KNRTU-KAI's participation in international projects, associations, and TEMPUS programs. The document also provides details on international accreditation of academic programs, the new German-Russian Institute of Advanced Technologies, and KNRTU-KAI's approach to developing new curricula/modules based on the qualifications framework of the European Higher Education Area.
How to design a miniature train set that always loops back well? Two question...
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TSU
The document discusses the mathematics education program at Tver State University. It provides data on the number of students in various degree programs in 1977 and 1994. It then discusses the requirements for the mathematics and computer science minor program, including necessary courses, assessments, credits, and workload. Finally, it compares the university's program to the standards of the SEFI (Société Européenne pour la Formation des Ingénieurs) organization at different levels, noting some additional topics that are not covered in the SEFI standards.
LETI - Mr. Posov
The document summarizes courses in discrete mathematics and mathematical logic/theory of algorithms taught at Saint Petersburg Electrotechnical University. The courses cover topics like number theory, combinatorics, binary relations, propositional/predicate logic, formal languages, and graph algorithms. Assessment includes individual homework assignments, tests, short in-lecture assignments, and written examinations with non-standard problems testing understanding. An alternative examination option allows converting a good first test mark to a programming, problem-solving, or essay assignment.
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