Advanced Modeling for Discrete Optimization
- 4.9
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Course Summary
This course is designed to teach advanced modeling techniques for data analysis, with a focus on linear and nonlinear models, time series analysis, and data visualization.Key Learning Points
- Learn how to build predictive models using linear and nonlinear regression techniques
- Explore time series analysis and forecasting methods
- Develop skills in data visualization and communication
Related Topics for further study
Learning Outcomes
- Develop advanced modeling skills for data analysis
- Gain expertise in linear and nonlinear models
- Create effective data visualizations and communicate insights to stakeholders
Prerequisites or good to have knowledge before taking this course
- Intermediate knowledge of statistics
- Familiarity with statistical software like R or Python
Course Difficulty Level
AdvancedCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Applied Machine Learning
- Data Science Essentials
- Big Data Analytics
Related Education Paths
Related Books
Description
Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars. Similar problems also underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional undergraduate computer science methods.
Outline
- Debugging and Improving Models
- Welcome to Advanced Modeling for Discrete Optimization
- 2.1.1 Model Debugging
- 2.1.2 Tracing Models
- 2.1.3 Relational Semantics
- 2.1.4 Too Many Solutions
- 2.1.5 Missing Solutions
- 2.1.6 Basic Model Improvement
- 2.1.7 Module 1 Summary
- Workshop 5 Solution
- Assignment Submission - IDE
- Assignment Submission - CLI
- Reference 1: Basic Features
- Reference 2: Booleans Expressions
- Reference 3: Sets, Arrays and Comprehensions
- Reference 4: Enumerated Types
- Reference 5: Strings and Output
- Reference 6: Option Types
- Reference 7: Predicates
- Reference 8: Flattening
- Reference 9: Transforming Data
- Reference 10: User Defined Functions
- Reference 11: Command Line Interface
- Course Overview
- Start of Course Survey
- “Building Decision Support Systems using MiniZinc” by Professor Mark Wallace
- Getting MiniZinc
- Workshop 5: Poetry Challenge
- About the Reference Material
- Predicates
- 2.2.1 Predicates
- 2.2.2 The let-in Construct
- 2.2.3 Using Predicates
- 2.2.4 Contexts
- 2.2.5 Module 2 Summary
- Workshop 6 Solution
- Workshop 6: Weighing an Elephant: Part 1
- Scheduling
- 2.3.1 Basic Scheduling
- 2.3.2 Disjunctive Scheduling
- 2.3.3 Cumulative Scheduling
- 2.3.4 Sequence Dependent Scheduling 1
- 2.3.5 Sequence Dependent Scheduling 2
- 2.3.6 Module 3 Summary
- Workshop 7 Solution
- Workshop 7: Visiting Zhuge Liang
- Packing
- 2.4.1 Square Packing
- 2.4.2 Rectilinear Packing without Rotation
- 2.4.3 Rectilinear Packing with Rotation
- Symmetry and Dominance
- 2.5.1 Symmetries and LexLeader
- 2.5.2 Matrix Model Symmetries
- 2.5.3 Value Symmetries
- 2.5.4 Dominance
- 2.5.5 Module 4 & 5 Summary
- Workshop 8 Solution
- Where to from here?
- Workshop 8: The Dieda Plasters
- End of Course Survey
Summary of User Reviews
Discover the Advanced Modeling course on Coursera, a highly-rated program that explores the intricacies of modeling. Users praise the course for its comprehensive content and expert instructors, making it a top choice for those looking to improve their modeling skills.Key Aspect Users Liked About This Course
The course is praised for its comprehensive content and expert instructors.Pros from User Reviews
- In-depth coverage of modeling techniques
- Expert instructors provide valuable insights and practical tips
- Flexible scheduling and self-paced learning options
- Opportunities to apply knowledge through hands-on exercises and projects
Cons from User Reviews
- Some users found the course challenging and require more time and effort to complete
- A few users experienced technical difficulties with the platform
- The course may not be suitable for beginners with no prior modeling experience
Prof. Jimmy Ho Man Lee
4.9 Raiting
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