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Iterative algorithms in machine learning refer to a class of algorithms that repeatedly update their parameters or models in a stepwise fashion until a certain convergence criteria are met. These algorithms are widely used in various machine learning tasks, particularly in optimization problems where the goal is to find the best set of parameters that minimize or maximize an objective function. Iterative algorithms are essential for training complex models, such as neural networks and gradient-boosted trees.

Here are some common iterative algorithms used in machine learning:

1. Gradient Descent: Gradient descent is a fundamental optimization algorithm used in training a wide range of machine learning models, including linear regression, logistic regression, and neural networks. It iteratively updates model parameters in the direction of the steepest descent of a cost function (usually the loss function) to find the optimal parameter values.

2. Stochastic Gradient Descent (SGD): SGD is a variant of gradient descent that updates model parameters using a random subset (mini-batch) of the training data at each iteration. This randomness can help escape local minima and converge faster, making it especially useful for large datasets.

3. Mini-Batch Gradient Descent: This is a compromise between standard gradient descent and SGD. It updates model parameters using small random subsets of the training data (mini-batches) at each iteration, striking a balance between convergence speed and computational efficiency.

4. Adam, RMSProp, and other Optimization Techniques: These are adaptive optimization algorithms that adjust the learning rate for each parameter based on its history of updates. They are commonly used to accelerate convergence and handle ill-conditioned optimization problems.

5. Expectation-Maximization (EM): EM is an iterative algorithm used in unsupervised learning, particularly for clustering and Gaussian Mixture Models (GMMs). It alternates between estimating hidden variables (expectation step) and updating model parameters (maximization step) to find the best-fitting model.

6. Alternating Least Squares (ALS): ALS is an iterative algorithm used in collaborative filtering and matrix factorization problems. It alternates between optimizing user and item latent factors to approximate the original matrix.

7. Boosting Algorithms: Boosting algorithms like AdaBoost and Gradient Boosting Machines (GBM) iteratively train weak learners (usually decision trees) and combine their predictions to form a strong ensemble model.

8. Conjugate Gradient: Conjugate gradient is an iterative optimization algorithm used in solving linear systems of equations and quadratic optimization problems.

9. Gibbs Sampling: Gibbs sampling is an iterative Markov Chain Monte Carlo (MCMC) algorithm used for approximate Bayesian inference in models with complex dependencies, such as Bayesian networks.

10. Coordinate Descent: This optimization technique iteratively updates one coordinate (or parameter) at a time while keeping others fixed. It is used in Lasso and Elastic Net regularization, among others.

The choice of iterative algorithm depends on the specific machine learning task, the type of model being trained, and the characteristics of the data. The goal of these algorithms is to converge to a set of parameters that optimally fit the data or minimize a given objective function.

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