Introduction To Statistical Machine Learning

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Aims and Objectives:Machine learning studies methods that can automatically detect patterns in data, and then use these patterns to predict future data or other outcomes of interest. It is widely used across many scientific and engineering disciplines. This course covers statistical fundamentals of machine learning, with a focus on supervised learning and empirical risk minimisation. Both generative and discriminative learning frameworks are discussed and a variety of widely used classification algorithms are overviewed.

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning.

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

Introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning, to prepare students for advanced AI courses.

The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments.

This course provides a hands-on introduction to widely-used tools for data science. Topics include computational hardware and Linux; languages and packages for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services.

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as \"machine learning\"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.

If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors' website.

STAT-427 Statistical Machine Learning (3) Introduction to statistical concepts, models, and algorithms of machine learning. Explores supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis, and related topics such as discriminant analysis, splines, lasso and other shrinkage methods, bootstrap, regression, and classification trees, and support vector machines, along with their tuning, diagnostics, and performance evaluation. Crosslist: STAT-627 . Prerequisite: STAT-415 .

In this chapter, an overview of the theory of probability, statistical and machine learning is made covering the main ideas and the most popular and widely used methods in this area. As a starting point, the randomness and determinism as well as the nature of the real-world problems are discussed. Then, the basic and well-known topics of the traditional probability theory and statistics including the probability mass and distribution, probability density and moments, density estimation, Bayesian and other branches of the probability theory, are recalled followed by a analysis. The well-known data pre-processing techniques, unsupervised and supervised machine learning methods are covered. These include a brief introduction of the distance metrics, normalization and standardization, feature selection, orthogonalization as well as a review of the most representative clustering, classification, regression and prediction approaches of various types. In the end, the topic of image processing is also briefly covered including the popular image transformation techniques, and a number of image feature extraction techniques at three different levels.

Stanford's \"Introduction to Statistics\" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

STAT 180 Introduction to Data Science (4) RSNSurvey course introducing the essential elements of data science: data collection, management, curation, and cleaning; summarizing and visualizing data; basic ideas of statistical inference, machine learning. Students will gain hands-on experience through computing labs. Offered: AWSp.View course details in MyPlan: STAT 180

STAT 302 Statistical Computing (3)An introduction to the foundations of statistical computing and data analysis. Topics include programming fundamentals, data cleaning, data visualization, debugging, and version control. Topics are motivated by methods in statistics and machine learning. Taught using the R programming language. Prerequisite: either STAT 311, STAT 390, or Q SCI 381; recommended: previous coursework in R programming language.View course details in MyPlan: STAT 302

STAT 391 Quantitative Introductory Statistics for Data Science (4)The basic concepts of statistics, machine learning and data science, as well as their computational aspects. Statistical models, likelihood, maximum likelihood and Bayesian estimation, regression, classification, clustering, principal component analysis, model validation, statistical testing. Practical implementation and visualization in data analysis. Assumes knowledge of basic probability, mathematical maturity, and ability to program. Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395. Offered: Sp.View course details in MyPlan: STAT 391

STAT 416 Introduction to Machine Learning (4) NScProvides practical introduction to machine learning. Modules include regression, classification, clustering, retrieval, recommender systems, and deep learning, with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets. Prerequisite: either CSE 123, CSE 143, CSE 160, or CSE 163; and either STAT 311, STAT 390, STAT 391, IND E 315, MATH 394/STAT 394, STAT 395/MATH 395, or Q SCI 381. Offered: jointly with CSE 416.View course details in MyPlan: STAT 416

STAT 435 Introduction to Statistical Machine Learning (4)Introduces the theory and application of statistical machine learning. Topics may include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; regression and classification; regularization and shrinkage approaches; non-linear approaches; tree-based methods; and support vector machines. Includes applications in R. Prerequisite: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 208. Offered: Sp.View course details in MyPlan: STAT 435

STAT 535 Statistical Learning: Modeling, Prediction, and Computing (3)Covers statistical learning over discrete multivariate domains, exemplified by graphical probability models. Emphasizes the algorithmic and computational aspects of these models. Includes additional topics in probability and statistics of discrete structures, general purpose discrete optimization algorithms like dynamic programming and minimum spanning tree, and applications to data analysis. Prerequisite: experience with programming in a high level language. Offered: A.View course details in MyPlan: STAT 535

STAT 538 Statistical Learning: Modeling, Prediction, and Computing (3)Reviews optimization and convex optimization in its relation to statistics. Covers the basics of unconstrained and constrained convex optimization, basics of clustering and classification, entropy, KL divergence and exponential family models, duality, modern learning algorithms like boosting, support vector machines, and variational approximations in inference. Prerequisite: experience with programming in a high level language. Offered: W.View course details in MyPlan: STAT 538 59ce067264