Transcription of Machine Learning Basics
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Machine Learning Basics
Machine Learning Basics Lecture slides for Chapter 5 of Deep Learning Ian Goodfellow 2016-09-26. TER 5. Machine Learning Basics . Linear Regression Linear regression example Optimization of w 3 2 1. MSE(train). 0. y 1. 2 3 x1 w1. Figure e : A linear regression problem, with a training set consisting of ten data p containing one feature. Because there is only one feature, the weight vect ns only a single parameter to learn, w . (Left)Observe that linear regression l (Goodfellow 2016). more parameters than training examples. We have little chance of ch Underfitting and Overfitting in tion that generalizes well when so many wildly di erent solutions ex xample, the quadratic model is perfectly matched to the true struct Polynomial Estimation sk so it generalizes well to new data.
pseudoinverse to solve the underdetermined problem with minimal regularization), the degree-9 polynomial overfits significantly, as we saw in figure 5.2. 119 Figure 5.5 (Goodfellow 2016) Bias and Variance CHAPTER 5. MACHINE LEARNING BASICS
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