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Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised learning.

This topic (Machine Learning) including Single-parameter linear regression, multi-parameter linear regression, Octave tutorial, logistic regression, regularization, neural network, machine learning system design, SVM (Support Vector Machines support

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Motivation 1: Data Compression ( Motivation 1- Data Compression ) The so-called data compression is to reduce the dimension of high-dimensional

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. deciding what to try next ( Determine what to do next ) I have already introduced some machine learning methods. It is obviously not enough to know

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. The problem of overfitting ( Over-fitting ) Back to the linear regression problem that we first mentioned to predict the relationship between housing

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price

1. Find the costfunction to measure the error 2. Fit the theta parameter to minimize the costfunction. Uses gradient descent, iterates n times, iteratively updates Theta, and reduces costfunction 3. Find the appropriate parameter theta for

This paper uses the regularization linear regression model pre-flow (water flowing out of dam) according to the water storage line (water level) of the reservoir, then the Debug Learning Algorithm and discusses the influence of deviation and

This article covers the following topics: Single-Variable linear regression Cost function Gradient Descent Single-Variable linear regressionLooking back at the next section, in the regression problem, we have given the input

16.1 problem formalization16.2 Content-based recommender system16.3 Collaborative Filtering16.4 Collaborative filtering algorithm16.5 vectorization: Low-rank matrix decomposition16.6 Implementation of work Details: Normalization of the mean value

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization,

19.1 Summary and acknowledgements Welcome to the last video on machine learning. We have been studying together for a long time. In the final video, I want to take a quick look at the main content of this course, and then briefly say a few words to

To draw a full stop to the first four sessions of the course, here are two of the models that were mentioned in the first four lectures by Andrew the Great God.The Perceptron Learning Algorithm Sensing machineModel:From the model, the Perceptron is

9. Clustering Content 9. Clustering 9.1 Supervised learning and unsupervised learning 9.2 K-means algorithm 9.3 Optimization Objective 9.4 Random Initialization 9.5 Choosing the number of Clusters 9.1 Supervised

The problem of regression is raised First, it needs to be clear that the fundamental purpose of the regression problem is prediction. For a problem, it is generally impossible to measure every situation (too much work), so we measure a set of

Supervised Learning Learn a function H: X → y H is called a hypothesis. 1. Linear Regression In this example, X is a two-dimensional vector, x1 represents living area, and x2 represents bedrooms. Functions/hypotheses H Set X0 = 1. Now,

23:55:01 | category: foreign university courses | Tag: machine learning | font size subscription INSTRUCTOR: Andrew Ng Http://see.stanford.edu/see/courseinfo.aspx? Coll = 348ca38a-3a6d-4052-937d-cb017338d7b1

SummaryThe main content of this lesson is about copying generic data. Although it is implemented in C language and does not use the generic programming technology such as template in C ++, the effect is very good. This section describes the

Continue to learn public lessonsThe second lesson does a simple calculator as an example. Probably touch the following knowledge points:Explaining the XCODE4, I looked at the latest download is XCode8.Xcode created the project, singleviewapplication

We will learn how to systematically improve machine learning algorithms, tell you when the algorithm is not doing well, and describe how to ' debug ' your learning algorithms and improve their performance "best practices". To optimize machine