|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ml-regression
课程评论：没有评论
课程名称：机器学习：回归 课程概述：案例研究 - 预测房价 在这个案例研究中，您将创建模型，以根据输入特征（例如房屋面积、卧室和浴室数量等）预测连续值（房价）。回归的应用范围广泛，包括医疗中的健康结果预测、金融中的股票价格预测，以及高性能计算中的电力使用分析等。 在本课程中，您将探讨用于预测和特征选择的正则化线性回归模型。您将能够处理非常大的特征集，并在各种复杂性模型之间进行选择。此外，您还将分析数据的某些方面（如异常值）对所选模型和预测的影响。为适应这些模型，您将实现可扩展到大数据集的优化算法。 学习成果：完成本课程后，您将能够： - 描述回归模型的输入和输出。 - 比较和对比建模数据时的偏差和方差。 - 使用优化算法估计模型参数。 - 使用交叉验证调整参数。 - 分析模型的性能。 - 描述稀疏性概念以及LASSO如何导致稀疏解。 - 使用方法在模型之间进行选择。 - 利用模型进行预测。 - 使用房屋数据集建立价格预测回归模型。 - 在Python中实现这些技术。 课程大纲： 1. **简单线性回归** 本模块将介绍最基础的回归模型：将直线拟合到数据上。您将学习如何建立简单回归模型，并使用闭式解和迭代优化算法（如梯度下降）拟合模型，从而解释估计的模型参数并进行预测。您还将分析模型对异常值的敏感性。 2. **多元回归** 在此模块中，您将学习如何构建利用多个数据特征来进行预测的回归模型，包括适应更复杂关系的模型。您将实施多元回归模型的梯度下降算法。 3. **评估性能** 掌握线性回归模型及其参数估计后，您将学习如何评估模型在预测新数据时的表现，以及如何选择最佳表现的模型。您将探讨测量“损失”的概念以及偏差-方差权衡的重要性。 4. **岭回归** 本模块将探讨如何使用岭回归来处理过拟合问题。您将学习将拟合训练数据的度量与偏见项结合，以避免选择过拟合的函数。 5. **特征选择与Lasso** 在此模块中，您将探讨在多元回归中进行特征选择的重要性。您将学习 exhaustion search 和贪婪算法，并实现一种Lasso回归模型，该模型以不同于岭回归的方式进行隐性特征选择。 6. **结束语** 在课程结束时，我们将回顾所涵盖的内容，包括特定于回归的技术和整个专业领域的重要基础机器学习概念，并简要讨论未在本课程中涉及的重要回归技术。我们还将概述接下来的专业课程内容。
Part: 1
Title:Simple Linear Regression
Description: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".
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.
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.
Part: 2
Title:Multiple Regression
Description: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.
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.
Here, you will also implement a gradient descent algorithm for fitting a multiple regression model.
Part: 3
Title:Assessing Performance
Description: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.
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.
The concepts described in this module are key to all machine learning problems, well-beyond the regression setting addressed in this course.
Part: 4
Title:Ridge Regression
Description: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".
You will implement both cross-validation and gradient descent to fit a ridge regression model and select the regularization constant.
Part: 5
Title:Feature Selection & Lasso
Description:A fundamental machine learning task is to select amongst a set of features to include in a model. In this module, you will explore this idea in the context of multiple regression, and describe how such feature selection is important for both interpretability and efficiency of forming predictions.
To start, you will examine methods that search over an enumeration of models including different subsets of features. You will analyze both exhaustive search and greedy algorithms. Then, instead of an explicit enumeration, we turn to Lasso regression, which implicitly performs feature selection in a manner akin to ridge regression: A complex model is fit based on a measure of fit to the training data plus a measure of overfitting different than that used in ridge. This lasso method has had impact in numerous applied domains, and the ideas behind the method have fundamentally changed machine learning and statistics. You will also implement a coordinate descent algorithm for fitting a Lasso model.
Coordinate descent is another, general, optimization technique, which is useful in many areas of machine learning.
Part: 6
Title:Closing Remarks
Description:In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to regression, as well as foundational machine learning concepts that will appear throughout the specialization. We also briefly discuss some important regression techniques we did not cover in this course.
We conclude with an overview of what's in store for you in the rest of the specialization.
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.
