机器学习经典课程

Machine learning

本课属于计算机科学里的人工智能方向,主要包含机器学习、数据挖掘和统计模式识别等方面,涉及到神经网络、智能医疗、智能机器人、计算机视觉、文本理解(网络搜索、反垃圾邮件)、数据库挖掘等领域,是一门多领域学科交叉逆天无敌之大课。

斯坦福大学

Coursera

计算机

难(高级)

55 小时

Sponsored\Ad:本课程链接由Coursera和Linkshare共同提供
  • 中文, 英语, 西班牙语, 其他
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课程概况

本课属于计算机科学里的人工智能方向,主要包含机器学习、数据挖掘和统计模式识别等方面,涉及到神经网络、智能医疗、智能机器人、计算机视觉、文本理解(网络搜索、反垃圾邮件)、数据库挖掘等领域,是一门多领域学科交叉逆天无敌之大课。通过该课可以了解到最有效的机器学习技术,掌握这些技术能让机器人们为人类更好的工作。

课中将为你广泛介绍机器学习、数据挖掘和统计模式识别等方面的内容,主题包括:
1、监督式学习(参数/非参数算法、支持矢量机器、内核、神经网络);
2、非监督式学习(聚类、降维、推荐系统、深度学习);
3、机器学习的最佳实践(偏差/方差理论、机器学习和人工智能的创新过程)。

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

你将学到什么

Logistic Regression

Artificial Neural Network

Machine Learning (ML) Algorithms

Machine Learning

课程大纲

周1
完成时间为 2 小时
Introduction
Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being
explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.
5 个视频 (总计 42 分钟), 9 个阅读材料, 1 个测验
完成时间为 2 小时
Linear Regression with One Variable
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price
prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
7 个视频 (总计 70 分钟), 8 个阅读材料, 1 个测验
完成时间为 2 小时
Linear Algebra Review
This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the
course, especially as we begin to cover models with multiple variables.
6 个视频 (总计 61 分钟), 7 个阅读材料, 1 个测验

周2
完成时间为 3 小时
Linear Regression with Multiple Variables
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input
features. We also discuss best practices for implementing linear regression.
8 个视频 (总计 65 分钟), 16 个阅读材料, 1 个测验
完成时间为 5 小时
Octave/Matlab Tutorial
This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.
6 个视频 (总计 80 分钟), 1 个阅读材料, 2 个测验

周3
完成时间为 2 小时
Logistic Regression
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
7 个视频 (总计 71 分钟), 8 个阅读材料, 1 个测验
完成时间为 4 小时
Regularization
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce
regularization, which helps prevent models from overfitting the training data.
4 个视频 (总计 39 分钟), 5 个阅读材料, 2 个测验

周4
完成时间为 5 小时
Neural Networks: Representation
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
7 个视频 (总计 63 分钟), 6 个阅读材料, 2 个测验

周5
完成时间为 5 小时
Neural Networks: Learning
In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this
module, you will be implementing your own neural network for digit recognition.
8 个视频 (总计 78 分钟), 8 个阅读材料, 2 个测验

周6
完成时间为 5 小时
Advice for Applying Machine Learning
Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in
practice, and discuss the best ways to evaluate performance of the learned models.
7 个视频 (总计 63 分钟), 7 个阅读材料, 2 个测验
完成时间为 1 小时
Machine Learning System Design
To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we
discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
5 个视频 (总计 60 分钟), 3 个阅读材料, 1 个测验

周7
完成时间为 5 小时
Support Vector Machines
Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and
discuss how to use it in practice.
6 个视频 (总计 98 分钟), 1 个阅读材料, 2 个测验

周8
完成时间为 1 小时
Unsupervised Learning
We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that
enable us to learn groupings of unlabeled data points.
5 个视频 (总计 39 分钟), 1 个阅读材料, 1 个测验
完成时间为 4 小时
Dimensionality Reduction
In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning
algorithms as well as for visualizations of complex datasets.
7 个视频 (总计 67 分钟), 1 个阅读材料, 2 个测验

周9
完成时间为 2 小时
Anomaly Detection
Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in
manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution...
8 个视频 (总计 91 分钟), 1 个阅读材料, 1 个测验
完成时间为 4 小时
Recommender Systems
When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
6 个视频 (总计 58 分钟), 1 个阅读材料, 2 个测验

周10
完成时间为 1 小时
Large Scale Machine Learning
Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine
learning algorithms with large datasets.
6 个视频 (总计 64 分钟), 1 个阅读材料, 1 个测验

周11
完成时间为 1 小时
Application Example: Photo OCR
Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this
problem and how to analyze and improve the performance of such a system.
5 个视频 (总计 57 分钟), 1 个阅读材料, 1 个测验

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