| School/Faculty/Institute |
Gradutate School of Science and Engineering |
| Course Code |
BDA 541 |
| Course Title in English |
Optimization and Simulation |
| Course Title in Turkish |
Optimizasyon ve Simülasyon |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Advanced |
| Semester |
Spring |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
188 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
Basic probability knowledge |
| Co-requisites |
None |
| Registration Restrictions |
Graduate and advanced undergraduate students |
| Overall Educational Objective |
To learn powerful analytical techniques to discover "optimal" decisions, simulate risky situations, and communicate effectively the results of spreadsheet analytics. |
| Course Description |
The aim of the course is to help the students create optimization and simulation models, and analyze these models to provide insight regarding the assumptions, value drivers, and risks present in a business situation. The students will use the models to explore different ways to think about uncertainty, guide decision-making, and persuasively communicate analytical results. |
| Course Description in Turkish |
Bu dersin amacı, öğrencilerin optimizasyon ve simülasyon modelleri oluşturmalarına yardımcı olmak ve bu modelleri, varsayımlar, değer sürücüleri ve bir iş durumundaki riskler hakkında fikir vermek için analiz etmektir. Öğrenciler, modelleri, belirsizlik hakkında düşünmek, karar vermeye yönlendirmek ve analitik sonuçları ikna etmek için farklı yollar bulmak için kullanacaklardır. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) identify when optimization and/or simulation is needed
2) build an optimization model based on mathematical principles
3) build simulation model to analyze uncertainty in decision making
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
| 1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
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| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
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| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
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| 4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
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| 5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
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| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
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| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
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| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
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| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
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| 10) Understanding of social and environmental aspects of machine learning applications. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
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| |
Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
H |
Participation
|
| 2) |
Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
H |
Exam
|
| 3) |
Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
H |
Exam
|
| 4) |
Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
H |
HW
|
| 5) |
Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
S |
HW
|
| 6) |
Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
H |
Exam
|
| 7) |
Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
S |
HW
|
| 8) |
Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
N |
|
| 9) |
Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
S |
HW
|
| 10) |
Understanding of social and environmental aspects of machine learning applications. |
N |
|
| Prepared by and Date |
SEMRA AĞRALI , February 2024 |
| Course Coordinator |
SEMRA AĞRALI |
| Semester |
Spring |
| Name of Instructor |
Öğr. Gör. DICLE ASLAN |
Course Contents
| Week |
Subject |
| 1) |
Introduction to Optimization |
| 2) |
Linear Programming Basics |
| 3) |
Linear Programming |
| 4) |
Binary Programming Basics |
| 5) |
Binary Programming |
| 6) |
Integer Programming Basics |
| 7) |
Integer Programming |
| 8) |
Integer Programming Applications |
| 9) |
Introduction to Simulation |
| 10) |
Simulation Models I |
| 11) |
Simulation Models II |
| 12) |
Simulation Models III |
| 13) |
Applications of Simulation |
| 14) |
Simulation with Optimization |
Information Package / Course Catalogue