Optimization Algorithms in AI
( 30 Modules )

Module #1
Introduction to Optimization in AI
Overview of optimization in AI, importance, and applications

Module #2
Types of Optimization Problems
Understanding minimization, maximization, and constrained optimization problems

Module #3
Gradient Descent Fundamentals
Mathematical foundations of gradient descent, iterative process, and convergence

Module #4
Gradient Descent Variants
Exploring batch, stochastic, mini-batch, and momentum-based gradient descent

Module #5
Convergence Analysis of Gradient Descent
Theoretical analysis of convergence, rate of convergence, and learning rate schedules

Module #6
Introduction to Linear Programming
Basic concepts, graphical method, and Simplex algorithm

Module #7
Integer Programming and Mixed-Integer Programming
Formulation, relaxation, and cutting plane methods for integer programming

Module #8
Dynamic Programming
Bellmans principle, memoization, and applications in optimization

Module #9
Constraint Programming
Modeling, constraint propagation, and search strategies

Module #10
Evolutionary Algorithms
Introduction to genetic algorithms, evolution strategies, and genetic programming

Module #11
Swarm Intelligence
Particle swarm optimization, ant colony optimization, and bee colony optimization

Module #12
Simulated Annealing
Metropolis algorithm, annealing schedule, and applications

Module #13
Bayesian Optimization
Gaussian processes, acquisition functions, and hyperparameter tuning

Module #14
Derivative-Free Optimization
Overview of surrogate-based, response surface, and trust-region methods

Module #15
Surrogate-Assisted Optimization
Surrogate modeling, radial basis functions, and Kriging

Module #16
Multi-Objective Optimization
Pareto optimality, weighted sum method, and multi-objective evolutionary algorithms

Module #17
Optimization in Deep Learning
Optimization techniques for deep learning, including stochastic gradient descent and Adam

Module #18
Practical Optimization in AI
Case studies and hands-on exercises for optimization in AI applications

Module #19
Optimization for Reinforcement Learning
Introduction to optimization in reinforcement learning, including policy gradient methods

Module #20
Optimization for Computer Vision
Applications of optimization algorithms in computer vision, including image processing and object detection

Module #21
Optimization for Natural Language Processing
Applications of optimization algorithms in NLP, including language modeling and text classification

Module #22
Optimization for Robotics
Optimization techniques for robotics, including motion planning and control

Module #23
Optimization for Recommendation Systems
Applications of optimization algorithms in recommendation systems, including collaborative filtering

Module #24
Optimization for Time Series Analysis
Optimization techniques for time series analysis, including forecasting and anomaly detection

Module #25
Optimization for Unsupervised Learning
Applications of optimization algorithms in unsupervised learning, including clustering and dimensionality reduction

Module #26
Optimization for Feature Engineering
Optimization techniques for feature engineering, including feature selection and engineering

Module #27
Optimization for Hyperparameter Tuning
Optimization algorithms for hyperparameter tuning, including grid search and random search

Module #28
Optimization for Transfer Learning
Optimization techniques for transfer learning, including domain adaptation and fine-tuning

Module #29
Advanced Topics in Optimization
Exploring recent advances in optimization, including differentiable programming and optimization under uncertainty

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

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Optimization Algorithms in AI ( 30 Modules )

Optimization Algorithms in AI
( 30 Modules )