在其他模式识别问题中，训练数据由一组输入向量 x 组成，没有任何相应的目标值。此类无监督学习问题的目标可能是发现数据中相似示例的组，这称为聚类
模式识别和机器学习（Bishop，2006）
Supervised learning
Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.
Unsupervised learning
In other pattern recognition problems, the training data consists of a set of input vectors x without any corresponding target values. The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering
Pattern Recognition and Machine Learning (Bishop, 2006)
In supervised learning, the input x is provided with the expected outcome y (i.e., the output the model is supposed to produce when the input is x), which is often called the "class" (or "label") of the corresponding input x.
In unsupervised learning, the "class" of an example x is not provided. So, unsupervised learning can be thought of as finding "hidden structure" in unlabelled data set.
Approaches to supervised learning include:
Classification (1R, Naive Bayes, decision tree learning algorithm, such as ID3 CART, and so on)
Suppose you need to recognize which vehicle is a car and which one is a motorcycle.
In the supervised learning case, your input (training) dataset needs to be labelled, that is, for each input element in your input (training) dataset, you should specify if it represents a car or a motorcycle.
In the unsupervised learning case, you do not label the inputs. The unsupervised model clusters the input into clusters based e.g. on similar features/properties. So, in this case, there is are no labels like "car".
For instance, very often training a neural network is supervised learning: you're telling the network to which class corresponds the feature vector you're feeding.
Clustering is unsupervised learning: you let the algorithm decide how to group samples into classes that share common properties.
I have always found the distinction between unsupervised and supervised learning to be arbitrary and a little confusing. There is no real distinction between the two cases, instead there is a range of situations in which an algorithm can have more or less 'supervision'. The existence of semi-supervised learning is an obvious examples where the line is blurred.
I tend to think of supervision as giving feedback to the algorithm about what solutions should be preferred. For a traditional supervised setting, such as spam detection, you tell the algorithm "don't make any mistakes on the training set"; for a traditional unsupervised setting, such as clustering, you tell the algorithm "points that are close to each other should be in the same cluster". It just so happens that, the first form of feedback is a lot more specific than the latter.
In short, when someone says 'supervised', think classification, when they say 'unsupervised' think clustering and try not to worry too much about it beyond that.
Supervised learning is based on training a data sample from data source with correct classification already assigned. Such techniques are utilized in feedforward or MultiLayer Perceptron (MLP) models. These MLP has three distinctive characteristics:
One or more layers of hidden neurons that are not part of the input or output layers of the network that enable the network to learn and solve any complex problems
The nonlinearity reflected in the neuronal activity is differentiable and,
The interconnection model of the network exhibits a high degree of connectivity.
These characteristics along with learning through training solve difficult and diverse problems. Learning through training in a supervised ANN model also called as error backpropagation algorithm. The error correction-learning algorithm trains the network based on the input-output samples and finds error signal, which is the difference of the output calculated and the desired output and adjusts the synaptic weights of the neurons that is proportional to the product of the error signal and the input instance of the synaptic weight. Based on this principle, error back propagation learning occurs in two passes:
Forward Pass:
Here, input vector is presented to the network. This input signal propagates forward, neuron by neuron through the network and emerges at the output end of the network as output signal: y(n) = φ(v(n)) where v(n) is the induced local field of a neuron defined by v(n) =Σ w(n)y(n). The output that is calculated at the output layer o(n) is compared with the desired response d(n) and finds the error e(n) for that neuron. The synaptic weights of the network during this pass are remains same.
Backward Pass:
The error signal that is originated at the output neuron of that layer is propagated backward through network. This calculates the local gradient for each neuron in each layer and allows the synaptic weights of the network to undergo changes in accordance with the delta rule as:
Δw(n) = η * δ(n) * y(n).
This recursive computation is continued, with forward pass followed by the backward pass for each input pattern till the network is converged.
Supervised learning paradigm of an ANN is efficient and finds solutions to several linear and non-linear problems such as classification, plant control, forecasting, prediction, robotics etc.
Unsupervised Learning
Self-Organizing neural networks learn using unsupervised learning algorithm to identify hidden patterns in unlabelled input data. This unsupervised refers to the ability to learn and organize information without providing an error signal to evaluate the potential solution. The lack of direction for the learning algorithm in unsupervised learning can sometime be advantageous, since it lets the algorithm to look back for patterns that have not been previously considered. The main characteristics of Self-Organizing Maps (SOM) are:
It transforms an incoming signal pattern of arbitrary dimension into one or 2 dimensional map and perform this transformation adaptively
The network represents feedforward structure with a single computational layer consisting of neurons arranged in rows and columns. At each stage of representation, each input signal is kept in its proper context and,
Neurons dealing with closely related pieces of information are close together and they communicate through synaptic connections.
The computational layer is also called as competitive layer since the neurons in the layer compete with each other to become active. Hence, this learning algorithm is called competitive algorithm. Unsupervised algorithm in SOM works in three phases:
Competition phase:
for each input pattern x, presented to the network, inner product with synaptic weight w is calculated and the neurons in the competitive layer finds a discriminant function that induce competition among the neurons and the synaptic weight vector that is close to the input vector in the Euclidean distance is announced as winner in the competition. That neuron is called best matching neuron,
i.e. x = arg min ║x - w║.
Cooperative phase:
the winning neuron determines the center of a topological neighborhood h of cooperating neurons. This is performed by the lateral interaction d among the cooperative neurons. This topological neighborhood reduces its size over a time period.
Adaptive phase:
enables the winning neuron and its neighborhood neurons to increase their individual values of the discriminant function in relation to the input pattern through suitable synaptic weight adjustments,
Δw = ηh(x)(x –w).
Upon repeated presentation of the training patterns, the synaptic weight vectors tend to follow the distribution of the input patterns due to the neighborhood updating and thus ANN learns without supervisor.
Self-Organizing Model naturally represents the neuro-biological behavior, and hence is used in many real world applications such as clustering, speech recognition, texture segmentation, vector coding etc.
There are many answers already which explain the differences in detail. I found these gifs on codeacademy and they often help me explain the differences effectively.
Supervised Learning
Notice that the training images have labels here and that the model is learning the names of the images.
Unsupervised Learning
Notice that what's being done here is just grouping(clustering) and that the model doesn't know anything about any image.
Machine learning: It explores the study and construction of algorithms that can learn from and make predictions on data.Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions expressed as outputs,rather than following strictly static program instructions.
Supervised learning: It is the machine learning task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.
Unsupervised learning: It is learning without a teacher. One basic thing that you might want to do with data is to visualize it. It is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning. Unsupervised learning uses procedures that attempt to find natural partitions of patterns.
With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you.Under the Unsupervised learning methods no labeled examples are provided and there is no notion of the output during the learning process. As a result, it is up to the learning scheme/model to find patterns or discover the groups of the input data
You should use unsupervised learning methods when you need a large amount of data to train your models, and the willingness and ability to experiment and explore, and of course a challenge that isn’t well solved via more-established methods.With unsupervised learning it is possible to learn larger and more complex models than with supervised learning.Here is a good example on it
Supervised Learning: You give variously labelled example data as input, along with the correct answers. This algorithm will learn from it, and start predicting correct results based on the inputs thereafter. Example: Email Spam filter
Unsupervised Learning: You just give data and don't tell anything - like labels or correct answers. Algorithm automatically analyses patterns in the data. Example: Google News
Supervised learning: say a kid goes to kinder-garden. here teacher shows him 3 toys-house,ball and car. now teacher gives him 10 toys. he will classify them in 3 box of house,ball and car based on his previous experience. so kid was first supervised by teachers for getting right answers for few sets. then he was tested on unknown toys.
Unsupervised learning: again kindergarten example.A child is given 10 toys. he is told to segment similar ones. so based on features like shape,size,color,function etc he will try to make 3 groups say A,B,C and group them.
The word Supervise means you are giving supervision/instruction to machine to help it find answers. Once it learns instructions, it can easily predict for new case.
Unsupervised means there is no supervision or instruction how to find answers/labels and machine will use its intelligence to find some pattern in our data. Here it will not make prediction, it will just try to find clusters which has similar data.
In this, every input pattern that is used to train the network is associated with an output pattern, which is the target or the desired pattern. A teacher is assumed to be present during the learning process, when a comparison is made between the network's computed output and the correct expected output, to determine the error. The error can then be used to change network parameters, which result in an improvement in performance.
Unsupervised Learning
In this learning method, the target output is not presented to the network. It is as if there is no teacher to present the desired pattern and hence, the system learns of its own by discovering and adapting to structural features in the input patterns.
Supervised Learning: In this technique of learning, we are given a data set and the system already knows the correct output of the data set. So here, our system learns by predicting a value of its own. Then, it does an accuracy check by using a cost function to check how close its prediction was to the actual output.
Unsupervised Learning: In this approach, we have little or no knowledge of what our result would be. So instead, we derive structure from the data where we don't know effect of variable. We make structure by clustering the data based on relationship among the variable in data. Here, we don't have a feedback based on our prediction.
You have input x and a target output t. So you train the algorithm to generalize to the missing parts. It is supervised because the target is given. You are the supervisor telling the algorithm: For the example x, you should output t!
Unsupervised learning
Although segmentation, clustering and compression are usually counted in this direction, I have a hard time to come up with a good definition for it.
Let's take auto-encoders for compression as an example. While you only have the input x given, it is the human engineer how tells the algorithm that the target is also x. So in some sense, this is not different from supervised learning.
And for clustering and segmentation, I'm not too sure if it really fits the definition of machine learning (see other question).
Supervised Learning: You have labeled data and have to learn from that. e.g house data along with price and then learn to predict price
Unsupervised learning: you have to find the trend and then predict, no prior labels given. e.g different people in the class and then a new person comes so what group does this new student belong to.
In Supervised Learning we know what the input and output should be. For example , given a set of cars. We have to find out which ones red and which ones blue.
Whereas, Unsupervised learning is where we have to find out the answer with a very little or without any idea about how the output should be. For example, a learner might be able to build a model that detects when people are smiling based on correlation of facial patterns and words such as "what are you smiling about?".
Supervised learning can label a new item into one of the trained labels based on learning during training. You need to provide large numbers of training data set, validation data set and test data set. If you provide say pixel image vectors of digits along with training data with labels, then it can identify the numbers.
Unsupervised learning does not require training data-sets. In unsupervised learning it can group items into different clusters based on the difference in the input vectors. If you provide pixel image vectors of digits and ask it to classify into 10 categories, it may do that. But it does know how to labels it as you have not provided training labels.
Supervised Learning is basically where you have input variables(x) and output variable(y) and use algorithm to learn the mapping function from input to the output. The reason why we called this as supervised is because algorithm learns from the training dataset, the algorithm iteratively makes predictions on the training data. Supervised have two types-Classification and Regression. Classification is when the output variable is category like yes/no, true/false. Regression is when the output is real values like height of person, Temperature etc.
UN supervised learning is where we have only input data(X) and no output variables. This is called an unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
Types of unsupervised learning are clustering and Association.
Supervised Learning is basically a technique in which the training data from which the machine learns is already labelled that is suppose a simple even odd number classifier where you have already classified the data during training . Therefore it uses "LABELLED" data.
Unsupervised learning on the contrary is a technique in which the machine by itself labels the data . Or you can say its the case when the machine learns by itself from scratch.
In Simple Supervised learning is type of machine learning problem in which we have some labels and by using that labels we implement algorithm such as regression and classification .Classification is applied where our output is like in the form of 0 or 1 ,true/false,yes/no. and regression is applied where out put a real value such a house of price
Unsupervised Learning is a type of machine learning problem in which we don't have any labels means we have some data only ,unstructured data and we have to cluster the data (grouping of data)using various unsupervised algorithm
"The process of an algorithm learning from training dataset and predict the output. "
Accuracy of predicted output directly proportional to the training data (length)
Supervised learning is where you have input variables (x) (training dataset) and an output variable (Y) (testing dataset) and you use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
Major types:
Classification (discrete y-axis)
Predictive (continuous y-axis)
Algorithms:
Classification Algorithms:
Neural Networks
Naïve Bayes classifiers
Fisher linear discriminant
KNN
Decision Tree
Super Vector Machines
Predictive Algorithms:
Nearest neighbor
Linear Regression,Multi Regression
In simple words.. :) It's my understanding, feel free to correct. Supervised learning is, we know what we are predicting on the basis of provided data. So we have a column in the dataset which needs to be predicated. Unsupervised learning is, we try to extract meaning out of the provided dataset. We don't have clarity on what to be predicted. So question is why we do this?.. :) Answer is - the outcome of Unsupervised learning is groups/clusters(similar data together). So if we receive any new data then we associate that with the identified cluster/group and understand it's features.
supervised learning is where we know the output of the raw input, i.e the data is labelled so that during the training of machine learning model it will understand what it need to detect in the give output, and it will guide the system during the training to detect the pre-labelled objects on that basis it will detect the similar objects which we have provided in training.
Here the algorithms will know what's the structure and pattern of data. Supervised learning is used for classification
As an example, we can have a different objects whose shapes are square, circle, trianle our task is to arrange the same types of shapes the labelled dataset have all the shapes labelled, and we will train the machine learning model on that dataset, on the based of training dateset it will start detecting the shapes.
Un-supervised learning
Unsupervised learning is a unguided learning where the end result is not known, it will cluster the dataset and based on similar properties of the object it will divide the objects on different bunches and detect the objects.
Here algorithms will search for the different pattern in the raw data, and based on that it will cluster the data. Un-supervised learning is used for clustering.
As an example, we can have different objects of multiple shapes square, circle, triangle, so it will make the bunches based on the object properties, if a object has four sides it will consider it square, and if it have three sides triangle and if no sides than circle, here the the data is not labelled, it will learn itself to detect the various shapes
Machine learning is a field where you are trying to make machine to mimic the human behavior.
You train machine just like a baby.The way humans learn, identify features, recognize patterns and train himself, same way you train machine by feeding data with various features. Machine algorithm identify the pattern within the data and classify it into particular category.
Machine learning broadly divided into two category, supervised and unsupervised learning.
Supervised learning is the concept where you have input vector / data with corresponding target value (output).On the other hand unsupervised learning is the concept where you only have input vectors / data without any corresponding target value.
An example of supervised learning is handwritten digits recognition where you have image of digits with corresponding digit [0-9], and an example of unsupervised learning is grouping customers by purchasing behavior.
Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is.
Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning algorithm for face detection in images would try to define what a face is (round skin-like-colored disk, with dark area where you expect the eyes etc). A machine learning algorithm would not have such coded definition, but would "learn-by-examples": you'll show several images of faces and not-faces and a good algorithm will eventually learn and be able to predict whether or not an unseen image is a face.
This particular example of face detection is supervised, which means that your examples must be labeled, or explicitly say which ones are faces and which ones aren't.
In an unsupervised algorithm your examples are not labeled, i.e. you don't say anything. Of course, in such a case the algorithm itself cannot "invent" what a face is, but it can try to cluster the data into different groups, e.g. it can distinguish that faces are very different from landscapes, which are very different from horses.
Since another answer mentions it (though, in an incorrect way): there are "intermediate" forms of supervision, i.e. semi-supervised and active learning. Technically, these are supervised methods in which there is some "smart" way to avoid a large number of labeled examples. In active learning, the algorithm itself decides which thing you should label (e.g. it can be pretty sure about a landscape and a horse, but it might ask you to confirm if a gorilla is indeed the picture of a face). In semi-supervised learning, there are two different algorithms which start with the labeled examples, and then "tell" each other the way they think about some large number of unlabeled data. From this "discussion" they learn.
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监督学习是指为算法提供的数据被“标记”或“标记”，以帮助您的逻辑做出决策。
示例：贝叶斯垃圾邮件过滤，您必须将项目标记为垃圾邮件才能优化结果。
无监督学习是一种算法，除了原始数据之外，无需任何外部输入即可尝试查找相关性。
示例：数据挖掘聚类算法。
Supervised learning is when the data you feed your algorithm with is "tagged" or "labelled", to help your logic make decisions.
Example: Bayes spam filtering, where you have to flag an item as spam to refine the results.
Unsupervised learning are types of algorithms that try to find correlations without any external inputs other than the raw data.
Example: data mining clustering algorithms.
监督学习
训练数据包含输入向量示例及其相应目标向量的应用被称为监督学习问题。
无监督学习
在其他模式识别问题中，训练数据由一组输入向量 x 组成，没有任何相应的目标值。此类无监督学习问题的目标可能是发现数据中相似示例的组，这称为聚类
模式识别和机器学习（Bishop，2006）
Supervised learning
Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.
Unsupervised learning
In other pattern recognition problems, the training data consists of a set of input vectors x without any corresponding target values. The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering
Pattern Recognition and Machine Learning (Bishop, 2006)
在监督学习中，输入
x提供了预期结果y（即，当输入为x时模型应该产生的输出>），通常称为相应输入x的“类”（或“标签”）。在无监督学习中，不提供示例
x的“类”。因此，无监督学习可以被认为是在未标记的数据集中寻找“隐藏结构”。监督学习的方法包括：
分类（1R、朴素贝叶斯、决策树学习算法等）
如ID3 CART等）
数值预测
无监督学习方法包括：
聚类（K-means、层次聚类）
关联规则学习
In supervised learning, the input
xis provided with the expected outcomey(i.e., the output the model is supposed to produce when the input isx), which is often called the "class" (or "label") of the corresponding inputx.In unsupervised learning, the "class" of an example
xis not provided. So, unsupervised learning can be thought of as finding "hidden structure" in unlabelled data set.Approaches to supervised learning include:
Classification (1R, Naive Bayes, decision tree learning algorithm, such
as ID3 CART, and so on)
Numeric Value Prediction
Approaches to unsupervised learning include:
Clustering (K-means, hierarchical clustering)
Association Rule Learning
我可以告诉你一个例子。
假设您需要识别哪辆车是汽车，哪辆车是摩托车。
在监督学习案例中，您的输入（训练）数据集需要被标记，也就是说，对于输入（训练）数据集中的每个输入元素，您应该指定它是否代表汽车或摩托车。
在无监督学习情况下，您不会标记输入。无监督模型基于例如相似的特征/属性将输入聚类成簇。因此，在这种情况下，不存在“汽车”之类的标签。
I can tell you an example.
Suppose you need to recognize which vehicle is a car and which one is a motorcycle.
In the supervised learning case, your input (training) dataset needs to be labelled, that is, for each input element in your input (training) dataset, you should specify if it represents a car or a motorcycle.
In the unsupervised learning case, you do not label the inputs. The unsupervised model clusters the input into clusters based e.g. on similar features/properties. So, in this case, there is are no labels like "car".
例如，训练神经网络通常是监督学习：您告诉网络哪个类对应于您正在输入的特征向量。
聚类是无监督学习：让算法决定如何将样本分组到具有共同属性的类中。
无监督学习的另一个例子是 Kohonen 的自组织映射。
For instance, very often training a neural network is supervised learning: you're telling the network to which class corresponds the feature vector you're feeding.
Clustering is unsupervised learning: you let the algorithm decide how to group samples into classes that share common properties.
Another example of unsupervised learning is Kohonen's self organizing maps.
我一直发现无监督学习和监督学习之间的区别是任意的并且有点令人困惑。这两种情况之间没有真正的区别，相反，在一系列情况下，算法可以具有或多或少的“监督”。半监督学习的存在就是一个界限模糊的明显例子。
我倾向于认为监督是向算法提供有关应该首选哪些解决方案的反馈。对于传统的监督设置，例如垃圾邮件检测，您告诉算法“不要在训练集上犯任何错误”；对于传统的无监督设置，例如聚类，您告诉算法“彼此靠近的点应该位于同一聚类中”。碰巧的是，第一种反馈形式比后者具体得多。
简而言之，当有人说“监督”时，请考虑分类，当他们说“无监督”时，请考虑聚类，并尽量不要过多担心除此之外的问题。
I have always found the distinction between unsupervised and supervised learning to be arbitrary and a little confusing. There is no real distinction between the two cases, instead there is a range of situations in which an algorithm can have more or less 'supervision'. The existence of semi-supervised learning is an obvious examples where the line is blurred.
I tend to think of supervision as giving feedback to the algorithm about what solutions should be preferred. For a traditional supervised setting, such as spam detection, you tell the algorithm "don't make any mistakes on the training set"; for a traditional unsupervised setting, such as clustering, you tell the algorithm "points that are close to each other should be in the same cluster". It just so happens that, the first form of feedback is a lot more specific than the latter.
In short, when someone says 'supervised', think classification, when they say 'unsupervised' think clustering and try not to worry too much about it beyond that.
监督学习
监督学习基于训练数据样本
来自已分配正确分类的数据源。
此类技术用于前馈或多层
感知器 (MLP) 模型。这些MLP具有三个独特的特征
特征：
或网络的输出层，使网络能够学习和
解决任何复杂的问题
可微，并且
连接性。
这些特征以及通过培训学习
解决困难和多样化的问题。学习通过
有监督的 ANN 模型的训练也称为误差反向传播算法。纠错学习
算法根据输入输出训练网络
采样并找到误差信号，这是
计算出的输出和所需的输出并调整
神经元的突触权重与
误差信号和输入实例的乘积
突触权重。基于这个原理，错误返回
传播学习分两遍进行：
前向传播：
这里，输入向量被呈现给网络。该输入信号通过网络逐个神经元向前传播，并出现在网络的输出端
网络作为输出信号：
y(n) = φ(v(n))，其中v(n)是由定义的神经元的感应局部场v(n) =Σ w(n)y(n)。将输出层 o(n) 计算的输出与所需响应d(n)进行比较，找到该神经元的错误e(n)。此过程中网络的突触权重保持不变。向后传递：
源自该层输出神经元的误差信号通过网络向后传播。这将计算每层中每个神经元的局部梯度，并允许网络的突触权重根据增量规则进行变化，如下所示：
继续递归计算，对每个输入模式进行前向传递，然后向后传递，直到网络已融合。
人工神经网络的监督学习范式非常高效，可以找到多种线性和非线性问题的解决方案，例如分类、工厂控制、预测、预测、机器人等。
无监督学习
自组织神经网络使用无监督进行学习用于识别未标记输入数据中隐藏模式的学习算法。这种无监督是指在不提供错误信号来评估潜在解决方案的情况下学习和组织信息的能力。在无监督学习中，学习算法缺乏方向有时可能是有利的，因为它可以让算法回顾以前没有考虑过的模式。自组织映射 (SOM) 的主要特点是：
一维或二维映射并自适应地执行此转换
计算层由按行排列的神经元组成
列。在表示的每个阶段，每个输入信号都被保留
在适当的背景下，
他们在一起并通过突触连接进行交流。
计算层也称为竞争层，因为该层中的神经元相互竞争以变得活跃。因此，这种学习算法称为竞争算法。 SOM 中的无监督算法
工作分三个阶段：
竞争阶段：
对于呈现给网络的每个输入模式
x，计算与突触权重w的内积，并计算竞争层中的神经元找到一个判别函数，引起神经元之间的竞争，并且在欧几里得距离中接近输入向量的突触权重向量被宣布为竞争的获胜者。该神经元称为最佳匹配神经元，合作阶段：
获胜的神经元决定合作神经元的拓扑邻域
h的中心。这是通过横向交互d来执行的合作神经元。这个拓扑邻域的大小会随着时间的推移而减小。
自适应阶段：
使获胜神经元及其邻近神经元能够增加与输入模式相关的判别函数的个体值
通过适当的突触权重调整，
在重复呈现训练模式时，由于邻域更新，突触权重向量倾向于遵循输入模式的分布，因此 ANN 无需监督即可学习。
自组织模型自然地代表了神经生物学行为，因此被用于许多现实世界的应用中，例如聚类、语音识别、纹理分割、矢量编码等。
参考。
Supervised Learning
Supervised learning is based on training a data sample
from data source with correct classification already assigned.
Such techniques are utilized in feedforward or MultiLayer
Perceptron (MLP) models. These MLP has three distinctive
characteristics:
or output layers of the network that enable the network to learn and
solve any complex problems
differentiable and,
connectivity.
These characteristics along with learning through training
solve difficult and diverse problems. Learning through
training in a supervised ANN model also called as error backpropagation algorithm. The error correction-learning
algorithm trains the network based on the input-output
samples and finds error signal, which is the difference of the
output calculated and the desired output and adjusts the
synaptic weights of the neurons that is proportional to the
product of the error signal and the input instance of the
synaptic weight. Based on this principle, error back
propagation learning occurs in two passes:
Forward Pass:
Here, input vector is presented to the network. This input signal propagates forward, neuron by neuron through the network and emerges at the output end of
the network as output signal:
y(n) = φ(v(n))wherev(n)is the induced local field of a neuron defined byv(n) =Σ w(n)y(n).The output that is calculated at the output layer o(n) is compared with the desired responsed(n)and finds the errore(n)for that neuron. The synaptic weights of the network during this pass are remains same.Backward Pass:
The error signal that is originated at the output neuron of that layer is propagated backward through network. This calculates the local gradient for each neuron in each layer and allows the synaptic weights of the network to undergo changes in accordance with the delta rule as:
This recursive computation is continued, with forward pass followed by the backward pass for each input pattern till the network is converged.
Supervised learning paradigm of an ANN is efficient and finds solutions to several linear and non-linear problems such as classification, plant control, forecasting, prediction, robotics etc.
Unsupervised Learning
Self-Organizing neural networks learn using unsupervised learning algorithm to identify hidden patterns in unlabelled input data. This unsupervised refers to the ability to learn and organize information without providing an error signal to evaluate the potential solution. The lack of direction for the learning algorithm in unsupervised learning can sometime be advantageous, since it lets the algorithm to look back for patterns that have not been previously considered. The main characteristics of Self-Organizing Maps (SOM) are:
one or 2 dimensional map and perform this transformation adaptively
computational layer consisting of neurons arranged in rows and
columns. At each stage of representation, each input signal is kept
in its proper context and,
together and they communicate through synaptic connections.
The computational layer is also called as competitive layer since the neurons in the layer compete with each other to become active. Hence, this learning algorithm is called competitive algorithm. Unsupervised algorithm in SOM
works in three phases:
Competition phase:
for each input pattern
x, presented to the network, inner product with synaptic weightwis calculated and the neurons in the competitive layer finds a discriminant function that induce competition among the neurons and the synaptic weight vector that is close to the input vector in the Euclidean distance is announced as winner in the competition. That neuron is called best matching neuron,Cooperative phase:
the winning neuron determines the center of a topological neighborhood
hof cooperating neurons. This is performed by the lateral interactiondamong thecooperative neurons. This topological neighborhood reduces its size over a time period.
Adaptive phase:
enables the winning neuron and its neighborhood neurons to increase their individual values of the discriminant function in relation to the input pattern
through suitable synaptic weight adjustments,
Upon repeated presentation of the training patterns, the synaptic weight vectors tend to follow the distribution of the input patterns due to the neighborhood updating and thus ANN learns without supervisor.
Self-Organizing Model naturally represents the neuro-biological behavior, and hence is used in many real world applications such as clustering, speech recognition, texture segmentation, vector coding etc.
Reference.
已经有很多答案详细解释了这些差异。我在 codeacademy 上找到了这些 gif，它们经常帮助我解释差异有效。
监督学习
请注意，训练图像在这里有标签，并且模型正在学习图像的名称。
无监督学习
请注意，这里所做的只是分组（聚类），模型对任何图像一无所知。
There are many answers already which explain the differences in detail. I found these gifs on codeacademy and they often help me explain the differences effectively.
Supervised Learning
Notice that the training images have labels here and that the model is learning the names of the images.
Unsupervised Learning
Notice that what's being done here is just grouping(clustering) and that the model doesn't know anything about any image.
机器学习：
它探索了可以从数据中学习和预测的算法的研究和构建。此类算法通过根据示例输入构建模型来进行操作，以便做出数据驱动的预测或表示为输出的决策，而不是遵循严格的静态程序指令。
监督学习：
它是从标记的训练数据中推断函数的机器学习任务。训练数据由一组训练示例组成。在监督学习中，每个示例都是由输入对象（通常是向量）和所需输出值（也称为监督信号）组成的对。监督学习算法分析训练数据并生成推断函数，该函数可用于映射新示例。
向计算机提供由“老师”给出的示例输入及其所需的输出，目标是学习将输入映射到输出的一般规则。具体来说，监督学习算法采用一组已知的输入数据以及对数据（输出）的已知响应，并训练模型以生成对新数据的响应的合理预测。
无监督学习：
这是无师自通的学习。一个基本的
您可能想要对数据做的事情是将其可视化。它是从未标记数据中推断描述隐藏结构的函数的机器学习任务。由于提供给学习者的示例未标记，因此没有错误或奖励信号来评估潜在的解决方案。这将无监督学习与监督学习区分开来。无监督学习使用尝试找到自然分区的过程
的图案。
无监督学习没有基于预测结果的反馈，即没有老师来纠正你。在无监督学习方法下，不提供标记示例，并且在学习过程中没有输出的概念。因此，由学习方案/模型来查找模式或发现输入数据的组
。
Machine learning:
It explores the study and construction of algorithms that can learn from and make predictions on data.Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions expressed as outputs,rather than following strictly static program instructions.
Supervised learning:
It is the machine learning task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.
Unsupervised learning:
It is learning without a teacher. One basic
thing that you might want to do with data is to visualize it. It is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning. Unsupervised learning uses procedures that attempt to find natural partitions
of patterns.
With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you.Under the Unsupervised learning methods no labeled examples are provided and there is no notion of the output during the learning process. As a result, it is up to the learning scheme/model to find patterns or discover the groups of the input data
.
监督学习：您提供各种标记的示例数据作为输入，以及正确的答案。该算法将从中学习，并开始根据此后的输入预测正确的结果。 示例：电子邮件垃圾邮件过滤器
无监督学习：您只需提供数据，而不告诉任何内容 - 例如标签或正确答案。算法自动分析数据中的模式。 示例：Google 新闻
Supervised Learning: You give variously labelled example data as input, along with the correct answers. This algorithm will learn from it, and start predicting correct results based on the inputs thereafter. Example: Email Spam filter
Unsupervised Learning: You just give data and don't tell anything - like labels or correct answers. Algorithm automatically analyses patterns in the data. Example: Google News
监督学习：
说一个孩子去幼儿园。这里老师给他展示了 3 个玩具——房子、球和汽车。现在老师给了他10个玩具。
他根据以前的经验将它们分为房子、球和汽车三个盒子。
因此，孩子首先受到老师的监督，以便在几组中得到正确的答案。然后他接受了未知玩具的测试。
无监督学习：
又是幼儿园的例子。给了一个孩子 10 个玩具。他被告知要分割相似的部分。
因此，根据形状、大小、颜色、功能等特征，他会尝试将 A、B、C 分为 3 组，然后将它们分组。
“监督”一词意味着您正在向机器提供监督/指令以帮助其找到答案。一旦它学习了指令，它就可以轻松预测新情况。
无监督意味着没有监督或指示如何查找答案/标签，机器将利用其智能在我们的数据中查找某些模式。这里它不会进行预测，它只会尝试找到具有相似数据的簇。
Supervised learning:
say a kid goes to kinder-garden. here teacher shows him 3 toys-house,ball and car. now teacher gives him 10 toys.
he will classify them in 3 box of house,ball and car based on his previous experience.
so kid was first supervised by teachers for getting right answers for few sets. then he was tested on unknown toys.
Unsupervised learning:
again kindergarten example.A child is given 10 toys. he is told to segment similar ones.
so based on features like shape,size,color,function etc he will try to make 3 groups say A,B,C and group them.
The word Supervise means you are giving supervision/instruction to machine to help it find answers. Once it learns instructions, it can easily predict for new case.
Unsupervised means there is no supervision or instruction how to find answers/labels and machine will use its intelligence to find some pattern in our data. Here it will not make prediction, it will just try to find clusters which has similar data.
监督学习，给定带有答案的数据。
给定标记为垃圾邮件/非垃圾邮件的电子邮件，学习垃圾邮件过滤器。
给定被诊断为患有糖尿病或未患有糖尿病的患者的数据集，学习将新患者分类为患有糖尿病或未患有糖尿病。
无监督学习，给定没有答案的数据，让计算机对事物进行分组。
给定在网络上找到的一组新闻文章，将其分组为关于同一故事的文章集。
给定自定义数据数据库，自动发现细分市场并将客户分组到不同的细分市场。
参考
Supervised learning, given the data with an answer.
Given email labeled as spam/not spam, learn a spam filter.
Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.
Unsupervised learning, given the data without an answer, let the pc to group things.
Given a set of news articles found on the web, group the into set of articles about the same story.
Given a database of custom data, automatically discover market segments and group customers into different market segments.
Reference
监督学习
无监督学习
Supervised Learning
Unsupervised Learning
我会尽量保持简单。
监督学习：在这种学习技术中，我们得到一个数据集，并且系统已经知道该数据集的正确输出。所以在这里，我们的系统通过预测自己的值来学习。然后，它通过使用成本函数来检查其预测与实际输出的接近程度来进行准确性检查。
无监督学习：在这种方法中，我们很少或根本不知道我们的结果会是什么。因此，我们从不知道变量影响的数据中导出结构。
我们根据数据中变量之间的关系对数据进行聚类来构建结构。
在这里，我们没有基于我们的预测的反馈。
I'll try to keep it simple.
Supervised Learning: In this technique of learning, we are given a data set and the system already knows the correct output of the data set. So here, our system learns by predicting a value of its own. Then, it does an accuracy check by using a cost function to check how close its prediction was to the actual output.
Unsupervised Learning: In this approach, we have little or no knowledge of what our result would be. So instead, we derive structure from the data where we don't know effect of variable.
We make structure by clustering the data based on relationship among the variable in data.
Here, we don't have a feedback based on our prediction.
监督学习
您有输入 x 和目标输出 t。因此，您训练算法以泛化到缺失的部分。因为目标被给定，所以受到监督。你是监督者，告诉算法：对于例子x，你应该输出t！
无监督学习
虽然分割、聚类和压缩通常都算在这个方向上，但我很难为它提出一个好的定义。
我们以用于压缩的自动编码器为例。虽然您只给出了输入 x，但人类工程师如何告诉算法目标也是 x。所以从某种意义上来说，这和监督学习没有什么不同。
对于聚类和分割，我不太确定它是否真的符合机器学习的定义（请参阅其他问题）。
Supervised learning
You have input x and a target output t. So you train the algorithm to generalize to the missing parts. It is supervised because the target is given. You are the supervisor telling the algorithm: For the example x, you should output t!
Unsupervised learning
Although segmentation, clustering and compression are usually counted in this direction, I have a hard time to come up with a good definition for it.
Let's take auto-encoders for compression as an example. While you only have the input x given, it is the human engineer how tells the algorithm that the target is also x. So in some sense, this is not different from supervised learning.
And for clustering and segmentation, I'm not too sure if it really fits the definition of machine learning (see other question).
监督学习：你已经标记了数据并且必须从中学习。例如，将数据与价格一起存储，然后学习预测价格
无监督学习：您必须找到趋势，然后进行预测，没有给出预先的标签。
例如，班上有不同的人，然后来了一个新人，那么这个新学生属于哪个组。
Supervised Learning: You have labeled data and have to learn from that. e.g house data along with price and then learn to predict price
Unsupervised learning: you have to find the trend and then predict, no prior labels given.
e.g different people in the class and then a new person comes so what group does this new student belong to.
在监督学习中，我们知道输入和输出应该是什么。例如，给定一组汽车。我们必须找出哪些是红色的，哪些是蓝色的。
然而，在无监督学习中，我们必须在很少或根本不知道输出应该如何的情况下找到答案。例如，学习者也许能够构建一个模型，根据面部模式和诸如“你在微笑什么？”等词语的相关性来检测人们何时微笑。
In Supervised Learning we know what the input and output should be. For example , given a set of cars. We have to find out which ones red and which ones blue.
Whereas, Unsupervised learning is where we have to find out the answer with a very little or without any idea about how the output should be. For example, a learner might be able to build a model that detects when people are smiling based on correlation of facial patterns and words such as "what are you smiling about?".
监督学习可以根据训练过程中的学习将新项目标记到经过训练的标签之一中。您需要提供大量的训练数据集、验证数据集和测试数据集。如果您提供数字的像素图像向量以及带有标签的训练数据，那么它就可以识别数字。
无监督学习不需要训练数据集。在无监督学习中，它可以根据输入向量的差异将项目分组到不同的簇中。如果你提供数字的像素图像向量并要求它分类为 10 个类别，它可能会这样做。但它确实知道如何标记它，因为您没有提供培训标签。
Supervised learning can label a new item into one of the trained labels based on learning during training. You need to provide large numbers of training data set, validation data set and test data set. If you provide say pixel image vectors of digits along with training data with labels, then it can identify the numbers.
Unsupervised learning does not require training data-sets. In unsupervised learning it can group items into different clusters based on the difference in the input vectors. If you provide pixel image vectors of digits and ask it to classify into 10 categories, it may do that. But it does know how to labels it as you have not provided training labels.
监督学习基本上是有输入变量（x）和输出变量（y）并使用算法来学习从输入到输出的映射函数。我们之所以将其称为监督，是因为算法从训练数据集中学习，算法迭代地对训练数据进行预测。
监督有两种类型——分类和回归。
分类是指输出变量是“是/否”、“真/假”等类别。
回归是指输出为真实值，如人的身高、温度等。
联合国监督学习是指我们只有输入数据（X）而没有输出变量。
这称为无监督学习，因为与上面的监督学习不同，没有正确答案，也没有老师。算法自行设计来发现并呈现数据中有趣的结构。
无监督学习的类型是聚类和关联。
Supervised Learning is basically where you have input variables(x) and output variable(y) and use algorithm to learn the mapping function from input to the output. The reason why we called this as supervised is because algorithm learns from the training dataset, the algorithm iteratively makes predictions on the training data.
Supervised have two types-Classification and Regression.
Classification is when the output variable is category like yes/no, true/false.
Regression is when the output is real values like height of person, Temperature etc.
UN supervised learning is where we have only input data(X) and no output variables.
This is called an unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
Types of unsupervised learning are clustering and Association.
监督学习基本上是一种技术，其中机器学习的训练数据已经被标记，假设有一个简单的偶数奇数分类器，您已经在训练期间对数据进行了分类。因此它使用“LABELLED”数据。
相反，无监督学习是一种机器自行标记数据的技术。或者你可以说机器从头开始自我学习就是这种情况。
Supervised Learning is basically a technique in which the training data from which the machine learns is already labelled that is suppose a simple even odd number classifier where you have already classified the data during training . Therefore it uses "LABELLED" data.
Unsupervised learning on the contrary is a technique in which the machine by itself labels the data . Or you can say its the case when the machine learns by itself from scratch.
简单来说
监督学习是一种机器学习问题，其中我们有一些标签，通过使用这些标签，我们实现回归和分类等算法。分类应用于我们的输出形式为
0 或 1，真/假，是/否。回归应用于输出真实值的地方，例如价格房屋
无监督学习是一种机器学习问题，其中我们没有任何标签意味着我们只有一些数据，非结构化数据和我们必须使用各种无监督算法对数据进行聚类（数据分组）
In Simple
Supervised learning is type of machine learning problem in which we have some labels and by using that labels we implement algorithm such as regression and classification .Classification is applied where our output is like in the form of
0 or 1 ,true/false,yes/no. and regression is applied where out put a real value such a house of price
Unsupervised Learning is a type of machine learning problem in which we don't have any labels means we have some data only ,unstructured data and we have to cluster the data (grouping of data)using various unsupervised algorithm
预测输出的准确性与训练数据（长度）
训练数据集）和输出变量（Y）（测试数据集），并使用算法来学习映射函数从输入到输出的
主要类型：
算法：
分类算法：
预测算法：
应用领域：
疾病与否
语音识别
预测HR是否选择特定候选人
预测股市价格
Accuracy of predicted output directly proportional to the training data (length)
Supervised learning is where you have input variables (x) (training dataset) and an output variable (Y) (testing dataset) and you use an algorithm to learn the mapping function from the input to the output.
Major types:
Algorithms:
Classification Algorithms:
Predictive Algorithms:
Application areas:
disease or not
Voice Recognition
Predict the HR select particular candidate or not
Predict the stock market price
监督学习：
一袋橙子
=>搭建模型
一袋苹果和橙子的混合袋。
=>请分类
无监督学习：
一袋苹果和橙子的混合。
=>构建模型
另一个混合包
=>请分类
Supervised Learning:
One bag with orange
=> build model
One mixed bag of apple and orange.
=> Please classify
Unsupervised Learning:
One mixed bag of apple and orange.
=> build model
Another mixed bag
=> Please classify
简单来说..：）这是我的理解，请随时纠正。
监督学习是，我们根据提供的数据知道我们正在预测什么。因此，我们在数据集中有一列需要进行谓词。
无监督学习是，我们尝试从提供的数据集中提取含义。我们不清楚要预测什么。所以问题是我们为什么这样做？..:) 答案是 - 无监督学习的结果是组/集群（相似的数据在一起）。因此，如果我们收到任何新数据，那么我们会将其与已识别的集群/组相关联并了解其特征。
我希望它能帮助你。
In simple words.. :) It's my understanding, feel free to correct.
Supervised learning is, we know what we are predicting on the basis of provided data. So we have a column in the dataset which needs to be predicated.
Unsupervised learning is, we try to extract meaning out of the provided dataset. We don't have clarity on what to be predicted. So question is why we do this?.. :) Answer is - the outcome of Unsupervised learning is groups/clusters(similar data together). So if we receive any new data then we associate that with the identified cluster/group and understand it's features.
I hope it will help you.
监督学习
监督学习是我们知道原始输入的输出，即数据被标记，以便在机器学习模型的训练过程中，它将了解需要在给定输出中检测什么，并在训练过程中指导系统。训练来检测预先标记的对象，在此基础上它将检测我们在训练中提供的相似对象。
在这里，算法将知道数据的结构和模式是什么。监督学习用于分类
举个例子，我们可以有不同的物体，其形状是正方形、圆形、三角形，我们的任务是排列相同类型的形状
标记的数据集具有标记的所有形状，我们将在该数据集上训练机器学习模型，根据训练数据集，它将开始检测形状。
无监督学习
无监督学习是一种最终结果未知的无指导学习，它将对数据集进行聚类，并根据对象的相似属性将对象划分为不同的束并检测对象。
这里，算法将搜索原始数据中的不同模式，并基于此对数据进行聚类。无监督学习用于聚类。
举个例子，我们可以有多种形状的不同对象，如正方形、圆形、三角形，所以它会根据对象属性来生成束，如果一个对象有四个边，它会认为它是正方形，如果它有三个边三角形和如果没有比圆更小的边，这里的数据没有被标记，它会学习自己检测各种形状
supervised learning
supervised learning is where we know the output of the raw input, i.e the data is labelled so that during the training of machine learning model it will understand what it need to detect in the give output, and it will guide the system during the training to detect the pre-labelled objects on that basis it will detect the similar objects which we have provided in training.
Here the algorithms will know what's the structure and pattern of data. Supervised learning is used for classification
As an example, we can have a different objects whose shapes are square, circle, trianle our task is to arrange the same types of shapes
the labelled dataset have all the shapes labelled, and we will train the machine learning model on that dataset, on the based of training dateset it will start detecting the shapes.
Un-supervised learning
Unsupervised learning is a unguided learning where the end result is not known, it will cluster the dataset and based on similar properties of the object it will divide the objects on different bunches and detect the objects.
Here algorithms will search for the different pattern in the raw data, and based on that it will cluster the data. Un-supervised learning is used for clustering.
As an example, we can have different objects of multiple shapes square, circle, triangle, so it will make the bunches based on the object properties, if a object has four sides it will consider it square, and if it have three sides triangle and if no sides than circle, here the the data is not labelled, it will learn itself to detect the various shapes
机器学习是一个试图让机器模仿人类行为的领域。
你就像婴儿一样训练机器。人类学习、识别特征、识别模式和训练自己的方式，与通过输入具有各种特征的数据来训练机器的方式相同。机器算法识别数据中的模式并将其分类为特定类别。
机器学习大致分为两类：监督学习和无监督学习。
监督学习是指您拥有具有相应目标值（输出）的输入向量/数据的概念。另一方面，无监督学习是您只有输入向量/数据而没有任何对应目标值的概念。
监督学习的一个例子是手写数字识别，其中有数字图像和相应的数字 [0-9]，无监督学习的一个例子是根据购买行为对客户进行分组。
Machine learning is a field where you are trying to make machine to mimic the human behavior.
You train machine just like a baby.The way humans learn, identify features, recognize patterns and train himself, same way you train machine by feeding data with various features. Machine algorithm identify the pattern within the data and classify it into particular category.
Machine learning broadly divided into two category, supervised and unsupervised learning.
Supervised learning is the concept where you have input vector / data with corresponding target value (output).On the other hand unsupervised learning is the concept where you only have input vectors / data without any corresponding target value.
An example of supervised learning is handwritten digits recognition where you have image of digits with corresponding digit [0-9], and an example of unsupervised learning is grouping customers by purchasing behavior.
既然你问了这个非常基本的问题，那么看起来有必要详细说明机器学习本身是什么。
机器学习是一类数据驱动的算法，即与“正常”算法不同，它是数据“告诉”“好答案”是什么。示例：用于图像中人脸检测的假设非机器学习算法将尝试定义人脸是什么（圆形皮肤色圆盘，在您期望的眼睛等处有暗区）。机器学习算法不会有这样的编码定义，但会“通过示例学习”：您将显示几张面孔和非面孔的图像，一个好的算法最终将学习并能够预测是否存在未见过的图像。图像是一张脸。
这个特定的人脸检测示例是监督，这意味着您的示例必须标记，或者明确说明哪些是人脸，哪些不是。
在无监督算法中，您的示例没有标记，即您什么也不说。当然，在这种情况下，算法本身无法“发明”脸部是什么，但它可以尝试聚类 将数据分成不同的组，例如它可以区分面孔与风景有很大不同，而风景与马有很大不同。
由于另一个答案提到了这一点（尽管以不正确的方式）：存在“中间”形式的监督，即半监督和主动学习。从技术上讲，这些是有监督的方法，其中有一些“智能”方式来避免大量标记的示例。在主动学习中，算法本身决定你应该标记哪个东西（例如，它可以非常确定风景和马，但它可能会要求你确认大猩猩是否确实是一张脸的图片）。在半监督学习中，有两种不同的算法，它们从标记的示例开始，然后“告诉”彼此它们对大量未标记数据的思考方式。他们从这次“讨论”中学习。
Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is.
Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning algorithm for face detection in images would try to define what a face is (round skin-like-colored disk, with dark area where you expect the eyes etc). A machine learning algorithm would not have such coded definition, but would "learn-by-examples": you'll show several images of faces and not-faces and a good algorithm will eventually learn and be able to predict whether or not an unseen image is a face.
This particular example of face detection is supervised, which means that your examples must be labeled, or explicitly say which ones are faces and which ones aren't.
In an unsupervised algorithm your examples are not labeled, i.e. you don't say anything. Of course, in such a case the algorithm itself cannot "invent" what a face is, but it can try to cluster the data into different groups, e.g. it can distinguish that faces are very different from landscapes, which are very different from horses.
Since another answer mentions it (though, in an incorrect way): there are "intermediate" forms of supervision, i.e. semi-supervised and active learning. Technically, these are supervised methods in which there is some "smart" way to avoid a large number of labeled examples. In active learning, the algorithm itself decides which thing you should label (e.g. it can be pretty sure about a landscape and a horse, but it might ask you to confirm if a gorilla is indeed the picture of a face). In semi-supervised learning, there are two different algorithms which start with the labeled examples, and then "tell" each other the way they think about some large number of unlabeled data. From this "discussion" they learn.