Master mathematical optimization methods to solve complex problems and make better decisions in engineering, business, and data science
Optimization techniques are essential tools for solving complex problems across various domains, from engineering to finance and artificial intelligence.
Learn to maximize performance and minimize costs in any system or process.
Understand the optimization algorithms that power modern AI systems.
Apply optimization to design better products and systems with limited resources.
Develop frameworks for making optimal decisions in uncertain environments.
Comprehensive coverage of optimization methods with practical applications and case studies.
Foundation of optimization techniques
Solving complex real-world problems
Discrete optimization techniques
Dealing with uncertainty
Advanced optimization methods
Balancing competing goals
Everything you need to know before enrolling in this optimization course
Don't have all prerequisites? Preparatory materials will be provided.
"Introduction to Linear Optimization" by Bertsimas and Tsitsiklis
"Numerical Optimization" by Nocedal and Wright
"Nonlinear Programming" by Dimitri Bertsekas
"Algorithms for Optimization" by Kochenderfer and Wheeler
Comprehensive notes provided so no need to worry !!.
12 weekly modules with 3-5 video lectures each (15-20 mins per video)
Weekly programming assignments with real-world datasets
MATLAB scripts are included for implementation.