Analytics and AI: Statistical Learning

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Analytics and AI: Statistical Learning

Course Description

The Analytics and AI: Statistical Learning course focuses on providing deeper understanding of AI approaches like K-nearest-neighbors, linear methods for regression and classification, tree-based methods, ensemble methods, neural networks, support vector machines, and K-means clustering. Participants will learn how to connect theoretical insights underlying common AI models and algorithms used for predictive modeling with practical implementation using statistical software like R and Python. 

Course Content

Basics of statistical learning-based AI

  • Data science and data mining
  • Supervised learning
  • Cross-validation

Linear models

  • Linear regression for estimation and inference, including model and variable selection, James-Stein estimator, shrinkage method, ridge regression, Last Absolute Shrinkage and Selection Operator (LASSO)
  • Principal components, partial least squares
  • Linear classification, including linear and quadratic discriminant analysis, naïve Bayes, logistic regression

Local smoothers and additive models

  • Locally Estimated Scatterplot Smoothing (LOESS), kernel smoothing,deterministic and stochastic design
  • Spline smoothing
  • Additive and generalized additive models

Tree-based and ensemble methods

  • Growing and pruning, practical issues
  • Bayes model averaging and stacking, bootstrapping, bagging
  • Random forests
  • Boosting, adaboosting

Advanced learning methods

  • Support vector machines, linearization by kernel function, regularization, multi-class
  • Neural networks, deep neural networks
  • Cluster analysis, k-means, expectation-maximization (EM) algorithm
Requirements & Materials

Prerequisites

RECOMMENDED:

  • Basic knowledge of linear regression
  • Some experience with R (statistical programming language)

REQUIRED:

  • Some Python programming proficiency
  • Basic calculus-based probability/statistics
  • Basic linear algebra

Materials

REQUIRED (Student must provide):

  • Internet connection
  • Free software (download and install before taking the course)
    • R statistical software (see cran.r-project.org) and R Studio (see rstudio.com/products/rstudio/download)
    • Python programming language (see www.python.org)
    • Adobe Acrobat PDF reader (see get.adobe.com/reader/)
  • Laptop or desktop computer (not a tablet)
  • Recommended, but not required: “An Introduction to Statistical Learning” (free textbook available at www.statlearning.com)

PROVIDED (Student will receive):

  • All course lessons, assignments, solutions

Who Should Attend

This course is ideal for individuals who want to better leverage statistical-learning-based AI models by studying both the theory and practical implementation.

Business professional attending in-person professional development course

What You Will Learn

  • Advanced statistical learning-based AI approaches for data analysis and prediction, including supervised and unsupervised learning approaches
  • Methods for applying advanced approaches appropriately using common statistical software (e.g., Python and R)
  • Specific advanced techniques in linear regression and classification, tree-based methods, ensemble methods, neural networks, smoothing, and clustering
Analytics professional learning on computer and laptop

How You Will Benefit

  • Understand the theoretical and statistical aspects of several widely used statistical-learning-base AI methods for understanding data and making predictions.
  • Apply theoretical knowledge of statistical learning methods to more effectively analyze real-world datasets.
  • Learn how to exploit knowledge of the underlying theory when using common libraries (e.g., in Python and R) to perform analysis.
  • Gain hands-on experience with combining the theory and application of statistical learning-based AI.
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    Taught by Experts in the Field

TRAIN AT YOUR LOCATION

We enable employers to provide specialized, on-location training on their own timetables. Our world-renowned experts can create unique content that meets your employees' specific needs. We also have the ability to deliver courses via web conferencing or on-demand online videos. For 15 or more students, it is more cost-effective for us to come to you.

  • Save Money
  • Flexible Schedule
  • Group Training
  • Customize Content
  • On-Site Training
  • Earn a Certificate
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