Difference between revisions of "Machine Learning"

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-== Types of Machine Learning ==
+* [[Machine Learning (Andrew Ng Course)]]
+* [[Neural Networks (Geoffrey Hinton Course)]]
-* Supervised Learning
-** Regression Problem: Continuous valued output.
-** Classification Problem: Discrete valued output.
-* Unsupervised Learning
-** Clustering
-== Linear Regression ==
-=== Terminologies ===
-* $x^{(i)}_j$ feature vectors
-* $y^{(i)}$ outcomes
-* $h_\theta(x)$ the hypothesis
-* $J(\theta)$ the cost function
-* $\alpha$ the learning rate
-=== Advanced Optimization Algorithms ===
-There are advanced algorithms (from numerical computing) to minimize the cost function other than the '''gradient descent'''. For all of the following algorithms all we need to supply to the algorithm is a code to compute the function $J(\theta)$ (the cost function) and the partial derivatives of the cost function $\frac{\partial}{\partial \theta_i} J(\theta)$.
-# Conjugate gradient
-# BFGS
-# L-BFGS
-Advantages
-* No need to manually pick $\alpha$ (the learning rate in gradient descent)
-* Often faster than gradient descent
-Disadvantages
-* More complex
-== Classification Problem ==
-=== Logistic Regression ===
-* $h_\theta(x) = 1 / (1 + e^{-\theta^T x})$. Note $f(z) = 1 / (1 + e^{-z})$ is called the sigmoid function / logistic function.
-* $J(\theta) = - y \cdot \log h_\theta(x) - (1 - y) \cdot \log (1-h_\theta(x))$. This comes from Maximum Likelihood Estimation in Statistics.
-=== Multi-class Classification ===
-* One-vs-all (one-vs-rest): For $n$-class classification, train a logistic regression classifier $h_\theta^{(i)} (x)$ for each class $i = 1, \ldots, n$ to predict the probability that $y = i$. To make a prediction on a new input $x$, pick the class $i$ that maximizes $h_\theta^{(i)} (x)$.
-=== Cocktail Party Problem ===
-* Algorithm
-** [W, s, v] = svd((repmat(sum(x.*x, 1), size(x, 1), 1).*x)*x');

Difference between revisions of "Machine Learning"

Latest revision as of 17:20, 30 October 2016

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