【人工智能】<吴恩达-机器学习>监督学习&非监督学习

简介: 【1月更文挑战第26天】【人工智能】<吴恩达-机器学习>监督学习&非监督学习

 目录

一、机器学习概述 - What is machine learning ?:

二、机器学习分类 - Machine learning algorithms:

1.监督学习 - Supervised learning:

2.无监督学习 - Unsupervised learning:

Cocktail party problem algorithm:


一、机器学习概述 - What is machine learning ?:

    • Machine learning: Field of study that gives computers the ability to learn without being explicitly programmed.
    • Machine learning: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measure by P, improves with experience E.

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    The correct answer is the first choice.


    二、机器学习分类 - Machine learning algorithms:

      • Supervised learning
      • Unsupervised learning
      • Others: Reinforcenment learning, Recommender systems

      1.监督学习 - Supervised learning:

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             Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process. Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.

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        • Supervised Learning: right answers given
        • Regerssion: Predict continuous valued output (price)

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          • Classification: Discrete valued output (0 or 1)

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          The corrcet answer is the third choice.

          2.无监督学习 - Unsupervised learning:

          image.gif编辑

                 Unsupervised learning is a type of machine learning in which the algorithm is not provided with any pre-assigned labels or scores for the training data. As a result, unsupervised learning algorithms must first self-discover any naturally occurring patterns in that training data set. Common examples include clustering, where the algorithm automatically groups its training examples into categories with similar features, and principal component analysis, where the algorithm finds ways to compress the training data set by identifying which features are most useful for discriminating between different training examples, and discarding the rest. This contrasts with supervised learning in which the training data include pre-assigned category labels (often by a human, or from the output of non-learning classification algorithm). Other intermediate levels in the supervision spectrum include reinforcement learning, where only numerical scores are available for each training example instead of detailed tags, and semi-supervised learning where only a portion of the training data have been tagged.

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          The correct answer is the second choice and the third choice.

          Cocktail party problem algorithm:

          ICA(独立成分分析)

          image.gif编辑

                 “鸡尾酒会问题”(cocktailparty problem)是在计算机语音识别领域的一个问题,当前语音识别技术已经可以以较高精度识别一个人所讲的话,但是当说话的人数为两人或者多人时,语音识别率就会极大的降低,这一难题被称为鸡尾酒会问题。

                 该问题描述的是给定混合信号,如何分离出鸡尾酒会中同时说话的每个人的独立信号。当有N个信号源时,通常假设观察信号也有N个(例如N个麦克风或者录音机)。该假设意味着混合矩阵是个方阵,即J = D,其中D是输入数据的维数,J是系统模型的维数。要分离出鸡尾酒会中同时说话的每个人的独立信号。

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