Statistical machine learning is a subfield of machine learning that focuses on the development of algorithms that can learn from and make predictions or decisions based on data. It is closely linked to statistical methods, which are employed to draw insights and inferences from data.

Here are some key aspects of statistical machine learning:

1. Probabilistic Models: It deals with the creation of models that describe the underlying probability distributions that generate data. This includes generative models, Bayesian networks, and Gaussian processes.
2. Supervised Learning: This involves training models to predict an outcome based on input features, using labeled examples as a guide. Techniques like linear regression, logistic regression, support vector machines, and many others are used here.
3. Unsupervised Learning: Without labeled outcomes to guide the learning process, unsupervised learning tries to identify patterns and structures in the data. Clustering and dimensionality reduction are common tasks.
4. Regularization Techniques: Regularization methods like ridge regression and LASSO are used to prevent overfitting by adding constraints to the learning process.
5. Cross-Validation: This is a technique for assessing how well a model will generalize to unseen data by splitting the available data into training and validation sets.
6. Optimization Methods: Optimization is central to the training of many models, involving techniques like gradient descent to find the parameters that minimize a given loss function.
7. Bayesian Inference: This allows for the updating of beliefs about unknown parameters as more data becomes available, based on Bayes’ theorem.
8. Ensemble Learning: Methods like random forests and boosting combine multiple models to create a single, more robust prediction.
9. Deep Learning: While not exclusively a statistical method, deep learning (including neural networks) can be understood in a statistical context, especially when considering the underlying principles of function approximation and maximum likelihood estimation.

The field is multidisciplinary and draws on insights from statistics, computer science, mathematics, and more. Statistical machine learning is at the core of many modern technologies and applications, from natural language processing and computer vision to finance and healthcare.