Statistical Learning Theory Discipline

A Statistical Learning Theory Discipline is an Academic Discipline that focused on research and development of machine learning algorithms based on statistical models.

• Example(s):
  • Statistical Classification,
  • Machine Learning Regression,
  • Statistical Inference Theory.
  • …
• Counter-Example(s):
  • Information Retrieval,
  • Data Mining.
• See: Statistical Learning Framework, Supervised Learning, Unsupervised Learning, Online Learning, Reinforcement Learning, Dimensionality Reduction, Artificial Neural Network, Clustering Algorithm, Structured Prediction, Anomaly Detection.

---

---

References

2020

• (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Statistical_learning_theory Retrieved:2020-2-1.
  • Statistical learning theory is a framework for machine learning

    drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics.

2009

• (Lafferty & Wasserman, 2009) ⇒ John D. Lafferty, and Larry Wasserman. (2009). “Statistical Machine Learning - Course: 10-702." Spring 2009, Carnegie Mellon Institute.
  • Statistical Machine Learning is a second graduate level course in machine learning, assuming students have taken Machine Learning (10-701) and Intermediate Statistics (36-705). The term "statistical" in the title reflects the emphasis on statistical analysis and methodology, which is the predominant approach in modern machine learning.
• Yoav Freund (2009). “Statistical Machine Learning (Boosting). Course: UC San Diego, CSE254, Winter 2009. http://seed.ucsd.edu/mediawiki/index.php/CSE254

2008

• (Hutter, 2008) ⇒ Marcus Hutter (2008). "Introduction to Statistical Machine Learning". In: Machine Learning Summer School (MLSS-2008).
  • QUOTE: This course provides a broad introduction to the methods and practice of statistical machine learning, which is concerned with the development of algorithms and techniques that learn from observed data by constructing stochastic models that can be used for making predictions and decisions. Topics covered include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimization; overfitting, regularization, and validation.

2007

• (Berkeley Univerisyt, 2007) ⇒ http://www.stat.berkeley.edu/~statlearning/
  • Statistical machine learning merges statistics with the computational sciences --- computer science, systems science and optimization. Much of the agenda in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring statistical methodology to bear. Fields such as bioinformatics, artificial intelligence, signal processing, communications, networking, information management, finance, game theory and control theory are all being heavily influenced by developments in statistical machine learning.
  • The field of statistical machine learning also poses some of the most challenging theoretical problems in modern statistics, chief among them being the general problem of understanding the link between inference and computation.

---