Optimization Algorithms for AI
( 30 Modules )

Module #1
Introduction to Optimization Algorithms
Overview of optimization algorithms, their importance in AI, and types of optimization problems

Module #2
Mathematical Background
Review of linear algebra, calculus, and probability theory fundamentals

Module #3
Gradient Descent
Basic gradient descent algorithm, convergence analysis, and variants (momentum, NAG, Adagrad)

Module #4
Stochastic Gradient Descent
Stochastic gradient descent, mini-batching, and parallel computing

Module #5
Conjugate Gradient Method
Conjugate gradient method, its convergence properties, and applications

Module #6
Quasi-Newton Methods
Quasi-Newton methods (BFGS, SR1), their convergence properties, and applications

Module #7
Newtons Method
Newtons method, its convergence properties, and applications

Module #8
Line Search Methods
Line search methods, Wolfe conditions, and Armijo rule

Module #9
Trust Region Methods
Trust region methods, their convergence properties, and applications

Module #10
Constraint Optimization
Introduction to constraint optimization, Karush-Kuhn-Tucker conditions, and Lagrange multipliers

Module #11
Linear Programming
Linear programming, simplex method, and dual simplex method

Module #12
Integer Programming
Integer programming, branch and bound method, and cutting plane method

Module #13
Heuristics and Metaheuristics
Introduction to heuristics and metaheuristics, simulated annealing, and genetic algorithms

Module #14
Swarm Intelligence
Swarm intelligence, particle swarm optimization, and ant colony optimization

Module #15
Evolutionary Algorithms
Evolutionary algorithms, evolution strategies, and genetic programming

Module #16
Global Optimization
Global optimization, multi-start methods, and surrogate-based optimization

Module #17
Bayesian Optimization
Bayesian optimization, Gaussian processes, and acquisition functions

Module #18
Optimization in Machine Learning
Optimization in machine learning, model selection, and hyperparameter tuning

Module #19
Optimization in Deep Learning
Optimization in deep learning, stochastic gradient descent with momentum, and Adam optimizer

Module #20
Optimization in Reinforcement Learning
Optimization in reinforcement learning, policy gradient methods, and Q-learning

Module #21
Optimization in Computer Vision
Optimization in computer vision, image processing, and object detection

Module #22
Optimization in Natural Language Processing
Optimization in natural language processing, language models, and text classification

Module #23
Optimization in Robotics
Optimization in robotics, motion planning, and control systems

Module #24
Optimization in Healthcare
Optimization in healthcare, medical imaging, and disease diagnosis

Module #25
Optimization in Finance
Optimization in finance, portfolio optimization, and risk management

Module #26
Optimization in Energy Systems
Optimization in energy systems, power grid management, and renewable energy integration

Module #27
Optimization in Transportation
Optimization in transportation, traffic flow management, and route optimization

Module #28
Optimization in Logistics
Optimization in logistics, supply chain management, and inventory control

Module #29
Case Studies and Applications
Real-world case studies and applications of optimization algorithms in AI

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Optimization Algorithms for AI career

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