课程信息
4.8
3,857 个评分
751 个审阅
专项课程

第 2 门课程(共 4 门),位于

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为27 小时

建议:6 weeks of study, 5-8 hours/week...
可选语言

英语(English)

字幕:英语(English), 阿拉伯语(Arabic)...

您将获得的技能

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis
专项课程

第 2 门课程(共 4 门),位于

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为27 小时

建议:6 weeks of study, 5-8 hours/week...
可选语言

英语(English)

字幕:英语(English), 阿拉伯语(Arabic)...

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 1 小时

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....
Reading
5 个视频(共 20 分钟), 3 个阅读材料
Video5 个视频
Welcome!1分钟
What is the course about?3分钟
Outlining the first half of the course5分钟
Outlining the second half of the course5分钟
Assumed background4分钟
Reading3 个阅读材料
Important Update regarding the Machine Learning Specialization10分钟
Slides presented in this module10分钟
Reading: Software tools you'll need10分钟
完成时间(小时)
完成时间为 3 小时

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....
Reading
25 个视频(共 122 分钟), 5 个阅读材料, 2 个测验
Video25 个视频
Regression fundamentals: data & model8分钟
Regression fundamentals: the task2分钟
Regression ML block diagram4分钟
The simple linear regression model2分钟
The cost of using a given line6分钟
Using the fitted line6分钟
Interpreting the fitted line6分钟
Defining our least squares optimization objective3分钟
Finding maxima or minima analytically7分钟
Maximizing a 1d function: a worked example2分钟
Finding the max via hill climbing6分钟
Finding the min via hill descent3分钟
Choosing stepsize and convergence criteria6分钟
Gradients: derivatives in multiple dimensions5分钟
Gradient descent: multidimensional hill descent6分钟
Computing the gradient of RSS7分钟
Approach 1: closed-form solution5分钟
Approach 2: gradient descent7分钟
Comparing the approaches1分钟
Influence of high leverage points: exploring the data4分钟
Influence of high leverage points: removing Center City7分钟
Influence of high leverage points: removing high-end towns3分钟
Asymmetric cost functions3分钟
A brief recap1分钟
Reading5 个阅读材料
Slides presented in this module10分钟
Optional reading: worked-out example for closed-form solution10分钟
Optional reading: worked-out example for gradient descent10分钟
Download notebooks to follow along10分钟
Reading: Fitting a simple linear regression model on housing data10分钟
Quiz2 个练习
Simple Linear Regression14分钟
Fitting a simple linear regression model on housing data8分钟
2
完成时间(小时)
完成时间为 3 小时

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....
Reading
19 个视频(共 87 分钟), 5 个阅读材料, 3 个测验
Video19 个视频
Polynomial regression3分钟
Modeling seasonality8分钟
Where we see seasonality3分钟
Regression with general features of 1 input2分钟
Motivating the use of multiple inputs4分钟
Defining notation3分钟
Regression with features of multiple inputs3分钟
Interpreting the multiple regression fit7分钟
Rewriting the single observation model in vector notation6分钟
Rewriting the model for all observations in matrix notation4分钟
Computing the cost of a D-dimensional curve9分钟
Computing the gradient of RSS3分钟
Approach 1: closed-form solution3分钟
Discussing the closed-form solution4分钟
Approach 2: gradient descent2分钟
Feature-by-feature update9分钟
Algorithmic summary of gradient descent approach4分钟
A brief recap1分钟
Reading5 个阅读材料
Slides presented in this module10分钟
Optional reading: review of matrix algebra10分钟
Reading: Exploring different multiple regression models for house price prediction10分钟
Numpy tutorial10分钟
Reading: Implementing gradient descent for multiple regression10分钟
Quiz3 个练习
Multiple Regression18分钟
Exploring different multiple regression models for house price prediction16分钟
Implementing gradient descent for multiple regression10分钟
3
完成时间(小时)
完成时间为 2 小时

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....
Reading
14 个视频(共 93 分钟), 2 个阅读材料, 2 个测验
Video14 个视频
What do we mean by "loss"?4分钟
Training error: assessing loss on the training set7分钟
Generalization error: what we really want8分钟
Test error: what we can actually compute4分钟
Defining overfitting2分钟
Training/test split1分钟
Irreducible error and bias6分钟
Variance and the bias-variance tradeoff6分钟
Error vs. amount of data6分钟
Formally defining the 3 sources of error14分钟
Formally deriving why 3 sources of error20分钟
Training/validation/test split for model selection, fitting, and assessment7分钟
A brief recap1分钟
Reading2 个阅读材料
Slides presented in this module10分钟
Reading: Exploring the bias-variance tradeoff10分钟
Quiz2 个练习
Assessing Performance26分钟
Exploring the bias-variance tradeoff8分钟
4
完成时间(小时)
完成时间为 3 小时

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....
Reading
16 个视频(共 85 分钟), 5 个阅读材料, 3 个测验
Video16 个视频
Overfitting demo7分钟
Overfitting for more general multiple regression models3分钟
Balancing fit and magnitude of coefficients7分钟
The resulting ridge objective and its extreme solutions5分钟
How ridge regression balances bias and variance1分钟
Ridge regression demo9分钟
The ridge coefficient path4分钟
Computing the gradient of the ridge objective5分钟
Approach 1: closed-form solution6分钟
Discussing the closed-form solution5分钟
Approach 2: gradient descent9分钟
Selecting tuning parameters via cross validation3分钟
K-fold cross validation5分钟
How to handle the intercept6分钟
A brief recap1分钟
Reading5 个阅读材料
Slides presented in this module10分钟
Download the notebook and follow along10分钟
Download the notebook and follow along10分钟
Reading: Observing effects of L2 penalty in polynomial regression10分钟
Reading: Implementing ridge regression via gradient descent10分钟
Quiz3 个练习
Ridge Regression18分钟
Observing effects of L2 penalty in polynomial regression14分钟
Implementing ridge regression via gradient descent16分钟
4.8
751 个审阅Chevron Right
职业方向

38%

完成这些课程后已开始新的职业生涯
工作福利

83%

通过此课程获得实实在在的工作福利
职业晋升

12%

加薪或升职

热门审阅

创建者 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!

创建者 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!

讲师

Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

关于 University of Washington

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....

关于 Machine Learning 专项课程

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....
Machine Learning

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