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Excerpt from course description

Machine Learning and Forecasting - RE-SIT EXAM

Introduction

This course provides a thorough introduction to statistical, machine learning and forecasting techniques. The objective of this course is to present important statistical and machine learning methodologies that can be used to predict or classify outcomes.

Course content

  • Fundamental principles of statistical learning and forecasting techniques: bias/variance trade-off, cross validation techniques and pseudo out of sample methods.
  • Key machine learning algorithms, including, regression, time series processes, regularization, and classification.
  • The perceptron model and the principles of artificial neural networks, such as the multilayer perceptron model. 

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.