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次における4の2コース

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自分のスケジュールですぐに学習を始めてください。

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約36時間で修了

推奨:6 weeks of study, 5-8 hours/week...

英語

字幕:英語, 韓国語, アラビア語

習得するスキル

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis

次における4の2コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約36時間で修了

推奨:6 weeks of study, 5-8 hours/week...

英語

字幕:英語, 韓国語, アラビア語

シラバス - 本コースの学習内容

1
1時間で修了

Welcome

Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed, continuous-valued response. Regression is used in a massive number of applications ranging from predicting stock prices to understanding gene regulatory networks.<p>This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.

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5件のビデオ (合計20分), 3 readings
5件のビデオ
What is the course about?3 分
Outlining the first half of the course5 分
Outlining the second half of the course5 分
Assumed background4 分
3件の学習用教材
Important Update regarding the Machine Learning Specialization10 分
Slides presented in this module10 分
Reading: Software tools you'll need10 分
3時間で修了

Simple Linear Regression

Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".<p> In this module, we describe the high-level regression task and then specialize these concepts to the simple linear regression case. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Based on this fitted function, you will interpret the estimated model parameters and form predictions. You will also analyze the sensitivity of your fit to outlying observations.<p> You will examine all of these concepts in the context of a case study of predicting house prices from the square feet of the house.

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25件のビデオ (合計122分), 5 readings, 2 quizzes
25件のビデオ
Regression fundamentals: data & model8 分
Regression fundamentals: the task2 分
Regression ML block diagram4 分
The simple linear regression model2 分
The cost of using a given line6 分
Using the fitted line6 分
Interpreting the fitted line6 分
Defining our least squares optimization objective3 分
Finding maxima or minima analytically7 分
Maximizing a 1d function: a worked example2 分
Finding the max via hill climbing6 分
Finding the min via hill descent3 分
Choosing stepsize and convergence criteria6 分
Gradients: derivatives in multiple dimensions5 分
Gradient descent: multidimensional hill descent6 分
Computing the gradient of RSS7 分
Approach 1: closed-form solution5 分
Approach 2: gradient descent7 分
Comparing the approaches1 分
Influence of high leverage points: exploring the data4 分
Influence of high leverage points: removing Center City7 分
Influence of high leverage points: removing high-end towns3 分
Asymmetric cost functions3 分
A brief recap1 分
5件の学習用教材
Slides presented in this module10 分
Optional reading: worked-out example for closed-form solution10 分
Optional reading: worked-out example for gradient descent10 分
Download notebooks to follow along10 分
Reading: Fitting a simple linear regression model on housing data10 分
2の練習問題
Simple Linear Regression14 分
Fitting a simple linear regression model on housing data8 分
2
3時間で修了

Multiple Regression

The next step in moving beyond simple linear regression is to consider "multiple regression" where multiple features of the data are used to form predictions. <p> More specifically, in this module, you will learn how to build models of more complex relationship between a single variable (e.g., 'square feet') and the observed response (like 'house sales price'). This includes things like fitting a polynomial to your data, or capturing seasonal changes in the response value. You will also learn how to incorporate multiple input variables (e.g., 'square feet', '# bedrooms', '# bathrooms'). You will then be able to describe how all of these models can still be cast within the linear regression framework, but now using multiple "features". Within this multiple regression framework, you will fit models to data, interpret estimated coefficients, and form predictions. <p>Here, you will also implement a gradient descent algorithm for fitting a multiple regression model.

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19件のビデオ (合計87分), 5 readings, 3 quizzes
19件のビデオ
Polynomial regression3 分
Modeling seasonality8 分
Where we see seasonality3 分
Regression with general features of 1 input2 分
Motivating the use of multiple inputs4 分
Defining notation3 分
Regression with features of multiple inputs3 分
Interpreting the multiple regression fit7 分
Rewriting the single observation model in vector notation6 分
Rewriting the model for all observations in matrix notation4 分
Computing the cost of a D-dimensional curve9 分
Computing the gradient of RSS3 分
Approach 1: closed-form solution3 分
Discussing the closed-form solution4 分
Approach 2: gradient descent2 分
Feature-by-feature update9 分
Algorithmic summary of gradient descent approach4 分
A brief recap1 分
5件の学習用教材
Slides presented in this module10 分
Optional reading: review of matrix algebra10 分
Reading: Exploring different multiple regression models for house price prediction10 分
Numpy tutorial10 分
Reading: Implementing gradient descent for multiple regression10 分
3の練習問題
Multiple Regression18 分
Exploring different multiple regression models for house price prediction16 分
Implementing gradient descent for multiple regression10 分
3
2時間で修了

Assessing Performance

Having learned about linear regression models and algorithms for estimating the parameters of such models, you are now ready to assess how well your considered method should perform in predicting new data. You are also ready to select amongst possible models to choose the best performing. <p> This module is all about these important topics of model selection and assessment. You will examine both theoretical and practical aspects of such analyses. You will first explore the concept of measuring the "loss" of your predictions, and use this to define training, test, and generalization error. For these measures of error, you will analyze how they vary with model complexity and how they might be utilized to form a valid assessment of predictive performance. This leads directly to an important conversation about the bias-variance tradeoff, which is fundamental to machine learning. Finally, you will devise a method to first select amongst models and then assess the performance of the selected model. <p>The concepts described in this module are key to all machine learning problems, well-beyond the regression setting addressed in this course.

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14件のビデオ (合計93分), 2 readings, 2 quizzes
14件のビデオ
What do we mean by "loss"?4 分
Training error: assessing loss on the training set7 分
Generalization error: what we really want8 分
Test error: what we can actually compute4 分
Defining overfitting2 分
Training/test split1 分
Irreducible error and bias6 分
Variance and the bias-variance tradeoff6 分
Error vs. amount of data6 分
Formally defining the 3 sources of error14 分
Formally deriving why 3 sources of error20 分
Training/validation/test split for model selection, fitting, and assessment7 分
A brief recap1 分
2件の学習用教材
Slides presented in this module10 分
Reading: Exploring the bias-variance tradeoff10 分
2の練習問題
Assessing Performance26 分
Exploring the bias-variance tradeoff8 分
4
3時間で修了

Ridge Regression

You have examined how the performance of a model varies with increasing model complexity, and can describe the potential pitfall of complex models becoming overfit to the training data. In this module, you will explore a very simple, but extremely effective technique for automatically coping with this issue. This method is called "ridge regression". You start out with a complex model, but now fit the model in a manner that not only incorporates a measure of fit to the training data, but also a term that biases the solution away from overfitted functions. To this end, you will explore symptoms of overfitted functions and use this to define a quantitative measure to use in your revised optimization objective. You will derive both a closed-form and gradient descent algorithm for fitting the ridge regression objective; these forms are small modifications from the original algorithms you derived for multiple regression. To select the strength of the bias away from overfitting, you will explore a general-purpose method called "cross validation". <p>You will implement both cross-validation and gradient descent to fit a ridge regression model and select the regularization constant.

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16件のビデオ (合計85分), 5 readings, 3 quizzes
16件のビデオ
Overfitting demo7 分
Overfitting for more general multiple regression models3 分
Balancing fit and magnitude of coefficients7 分
The resulting ridge objective and its extreme solutions5 分
How ridge regression balances bias and variance1 分
Ridge regression demo9 分
The ridge coefficient path4 分
Computing the gradient of the ridge objective5 分
Approach 1: closed-form solution6 分
Discussing the closed-form solution5 分
Approach 2: gradient descent9 分
Selecting tuning parameters via cross validation3 分
K-fold cross validation5 分
How to handle the intercept6 分
A brief recap1 分
5件の学習用教材
Slides presented in this module10 分
Download the notebook and follow along10 分
Download the notebook and follow along10 分
Reading: Observing effects of L2 penalty in polynomial regression10 分
Reading: Implementing ridge regression via gradient descent10 分
3の練習問題
Ridge Regression18 分
Observing effects of L2 penalty in polynomial regression14 分
Implementing ridge regression via gradient descent16 分
4.8
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Machine Learning: Regression からの人気レビュー

by PDMar 17th 2016

I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!

by CMJan 27th 2016

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

講師

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Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

ワシントン大学(University of Washington)について

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

機械学習の専門講座について

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
機械学習

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