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기계 학습 전문 분야

워싱턴 대학교

About this Course

4.8

4,169개의 평가

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795개의 리뷰

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.
In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.
Learning Outcomes: By the end of this course, you will be able to:
-Describe the input and output of a regression model.
-Compare and contrast bias and variance when modeling data.
-Estimate model parameters using optimization algorithms.
-Tune parameters with cross validation.
-Analyze the performance of the model.
-Describe the notion of sparsity and how LASSO leads to sparse solutions.
-Deploy methods to select between models.
-Exploit the model to form predictions.
-Build a regression model to predict prices using a housing dataset.
-Implement these techniques in Python.

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

일정에 따라 마감일을 재설정합니다.

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

자막: 영어, 한국어, 아랍어

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

일정에 따라 마감일을 재설정합니다.

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

자막: 영어, 한국어, 아랍어

주

1Regression 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....

5 videos (Total 20 min), 3 readings

Welcome!1m

What is the course about?3m

Outlining the first half of the course5m

Outlining the second half of the course5m

Assumed background4m

Important Update regarding the Machine Learning Specialization10m

Slides presented in this module10m

Reading: Software tools you'll need10m

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....

25 videos (Total 122 min), 5 readings, 2 quizzes

Regression fundamentals: data & model8m

Regression fundamentals: the task2m

Regression ML block diagram4m

The simple linear regression model2m

The cost of using a given line6m

Using the fitted line6m

Interpreting the fitted line6m

Defining our least squares optimization objective3m

Finding maxima or minima analytically7m

Maximizing a 1d function: a worked example2m

Finding the max via hill climbing6m

Finding the min via hill descent3m

Choosing stepsize and convergence criteria6m

Gradients: derivatives in multiple dimensions5m

Gradient descent: multidimensional hill descent6m

Computing the gradient of RSS7m

Approach 1: closed-form solution5m

Approach 2: gradient descent7m

Comparing the approaches1m

Influence of high leverage points: exploring the data4m

Influence of high leverage points: removing Center City7m

Influence of high leverage points: removing high-end towns3m

Asymmetric cost functions3m

A brief recap1m

Slides presented in this module10m

Optional reading: worked-out example for closed-form solution10m

Optional reading: worked-out example for gradient descent10m

Download notebooks to follow along10m

Reading: Fitting a simple linear regression model on housing data10m

Simple Linear Regression14m

Fitting a simple linear regression model on housing data8m

주

2The 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....

19 videos (Total 87 min), 5 readings, 3 quizzes

Polynomial regression3m

Modeling seasonality8m

Where we see seasonality3m

Regression with general features of 1 input2m

Motivating the use of multiple inputs4m

Defining notation3m

Regression with features of multiple inputs3m

Interpreting the multiple regression fit7m

Rewriting the single observation model in vector notation6m

Rewriting the model for all observations in matrix notation4m

Computing the cost of a D-dimensional curve9m

Computing the gradient of RSS3m

Approach 1: closed-form solution3m

Discussing the closed-form solution4m

Approach 2: gradient descent2m

Feature-by-feature update9m

Algorithmic summary of gradient descent approach4m

A brief recap1m

Slides presented in this module10m

Optional reading: review of matrix algebra10m

Reading: Exploring different multiple regression models for house price prediction10m

Numpy tutorial10m

Reading: Implementing gradient descent for multiple regression10m

Multiple Regression18m

Exploring different multiple regression models for house price prediction16m

Implementing gradient descent for multiple regression10m

주

3Having 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....

14 videos (Total 93 min), 2 readings, 2 quizzes

What do we mean by "loss"?4m

Training error: assessing loss on the training set7m

Generalization error: what we really want8m

Test error: what we can actually compute4m

Defining overfitting2m

Training/test split1m

Irreducible error and bias6m

Variance and the bias-variance tradeoff6m

Error vs. amount of data6m

Formally defining the 3 sources of error14m

Formally deriving why 3 sources of error20m

Training/validation/test split for model selection, fitting, and assessment7m

A brief recap1m

Slides presented in this module10m

Reading: Exploring the bias-variance tradeoff10m

Assessing Performance26m

Exploring the bias-variance tradeoff8m

주

4You 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....

16 videos (Total 85 min), 5 readings, 3 quizzes

Overfitting demo7m

Overfitting for more general multiple regression models3m

Balancing fit and magnitude of coefficients7m

The resulting ridge objective and its extreme solutions5m

How ridge regression balances bias and variance1m

Ridge regression demo9m

The ridge coefficient path4m

Computing the gradient of the ridge objective5m

Approach 1: closed-form solution6m

Discussing the closed-form solution5m

Approach 2: gradient descent9m

Selecting tuning parameters via cross validation3m

K-fold cross validation5m

How to handle the intercept6m

A brief recap1m

Slides presented in this module10m

Download the notebook and follow along10m

Download the notebook and follow along10m

Reading: Observing effects of L2 penalty in polynomial regression10m

Reading: Implementing ridge regression via gradient descent10m

Ridge Regression18m

Observing effects of L2 penalty in polynomial regression14m

Implementing ridge regression via gradient descent16m

4.8

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대학: PD•Mar 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!

대학: CM•Jan 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!

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....

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....

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