CMPE302 [machinLearning]

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What Will You Learn?

  • Machine learning concerns the construction of systems that can learn from data. We need learning every time we cannot directly write a computer program to solve a given task; e.g. programs that learn to recognize human faces, to digitalize an acoustic speech signal and/or written carachters, and to drive autonomous robots. This course covers the theory and practical algorithms for machine learning focusing on classification, clustering, dimensionality reduction and reinforcement learning.

Course Content

1 An introduction to Machine Learning

  • 1 An introduction to Machine Learning part1
    01:09:00
  • 1 An introduction to Machine Learning part2
    55:00

2 Linear Regression, Direct solution method, Gradient Descent algorithm

Multivariable linear regression

Gradient Descent Method

Polynomial Regression

Bias-Variance-Error decomposition-Expectation-Covariance

RBF – Nonlinear Regression

Information -Entropy-Cross Entropy

Perceptron Learning Algorithm

SupportVectorMachine

Logistic Regression Algorithm

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