Statistical and machine learning methods for engineering mechanics

The course is divided into four parts: basis of machine learning applications, statistical learning methods, machine learning methods and time series forecasting. Some more details of each part are given as below.

• Basis of machine learning applications:
  • Clarification of different terminologies within field of AI and ML
  • Overview of different machine learning categories
  • Basic mathematics and statistics for application of ML
• Statistical learning methods
  • Regression and its interpretation
  • Gradient for regression (parameter estimations)
  • Polynomial and Spline fitting
  • Generalized linear regression
  • Generalized additive model and mixed effect model
• Machine learning methods
  • Logistical regression and classification
  • Neural network
  • Support vector machine
  • Decision trees and ensemble algorithm
  • Boosting method (XGBoost)
• Machine learning methods
  • Gaussian transformation method
  • Basic properties of stationary Gaussian process
  • Autocorrelation and Conditional expectation
  • Auto regressive models and Moving average models
  • ARIMA models
  • Examples of applications

The course will contain some assignments and seminars related to different methods.

Please contact Wengang Mao. Email: wengang.mao@chalmers.se

Hastie T., Ribshirani R. and Friedman J. (2003). The elements of statistical learning, Data mining, inference and prediction. Springer.

Shalizi, C.R. (2019). Advanced data analysis from an Elementary point of view. Pre-print.

Shumway, R.H. and Stoffer, D.S. (2016). Time series analysis and its applications with R examples, Fourth edition. Springer.

Wei, W.W.S. (2006). Time series analysis Univariate and multivariate models, Second edition. Pearson Addison Wesley.

Wengang Mao Phone: 0317721483 Email: wengang.mao@chalmers.se