About this Course
Optimization without gradients: useful for scheduling and design and game AI. This comprehensive curriculum is designed to give you hands-on experience with Genetic Algorithms & Evolutionary Computation. This CS Students & Optimization Engineers curriculum is designed to give you hands-on experience and deep conceptual understanding.
Across 4 intensive modules, you'll tackle real-world challenges and build practical projects that reinforce your learning. By the end of this journey, you'll have the skills and proof of work to demonstrate your expertise.
What you'll learn
Master the core concepts of foundations of genetic algorithms & representation.
Gain hands-on experience with selection mechanisms, crossover & mutation operators.
Understand the architecture behind multi-objective optimization & nsga-ii.
Implement production-grade advanced evolutionary paradigms (gp, neat & co-evolution).
W1
Foundations of Genetic Algorithms & Representation
Master the core concepts of foundations of genetic algorithms & representation.
3 videos•84m
2 readings
3 topics
1 homework
W2
Selection Mechanisms, Crossover & Mutation Operators
Gain hands-on experience with selection mechanisms, crossover & mutation operators.
3 videos•34m
3 readings
3 topics
1 homework
W3
Multi-Objective Optimization & NSGA-II
Understand the architecture behind multi-objective optimization & nsga-ii.
3 videos•59m
3 readings
3 topics
1 homework
W4
Advanced Evolutionary Paradigms (GP, NEAT & Co-evolution)
Implement production-grade advanced evolutionary paradigms (gp, neat & co-evolution).
3 videos•34m
3 readings
3 topics
1 homework
01
Learn
Watch curated videos and read study resources
02
Practice
Practice what you learned
03
Build Projects
Build projects using your new gained knowledge
04
Submit & Verify
Submit your project and get verified by our system
References
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