You are a teacher that has to compile a set of slides including voiceover from a given text. The presentation length has to be 5 minutes. Use this text as input to create 6 slides including voiceover. The figures mentioned in the text should be part of the visuals. Text: "1.	Machine Learning Definition: Learning from Data
We start with two definitions of Machine Learning (ML): 
Machine learning is a field of computer science that gives computer systems the ability to “learn” […] with data, without being explicitly programmed. (A. Samuel 1959)
Or, more formally: 
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 measured by P improves with experience E. (T. Mitchell 1998)
Typical of Machine Learning (ML), then, is that such systems get "capabilities" that they "learn" from data. This is in contrast to “normal” software procedures, where a computer is provided with explicit rules in the form of  IF A is TRUE, THEN DO something
We can illustrate this well with the example of a spam detector for e-mails: A spam detector is a software that has the task of finding out whether a mail is unsolicited spam mail or not. The software must therefore be able to classify a mail. In the traditional approach, the programmers would come up with rules to detect spam. For example, they might try to look for certain keywords in the text or for errors in the language. The server from which the mail is sent could also be important. These rules can quickly become complex. They also change over time. 
With ML, on the other hand, these rules are not explicitly programmed. Instead, the system is first trained by feeding it mails that have already been assigned to the classes spam or not spam. ML methods can implicitly derive the spam detection rules. Based on these implicit rules the spam detector is then able to classify new, unknown mails as spam mails or not spam. In other words: The spam detector (“machine”) “learns” how to identify spam mails from data. This is why it is called machine learning.  
There are a few more terms that we can explain using the example of the spam Detector: Properties of a mail, such as sender, subject, date sent and content are examples of so-called features or variables. Depending on the data used to perform ML, features can be very different: from common data fields such as a customer's income to the data in an image or audio file. A feature is also the whether the mail is spam or not. This feature is to be predicted by the ML system. We call this variable the target variable. In our example, the target variable is a binary variable, i.e. it can have only two different values, TRUE or FALSE. Please note: ML is not superior to the traditional way of programming. Often, there are clear rules that can easily put into code. Or there are no proper data available to use ML methods. It is also possible to combine both approaches! 
2.	Types of Learning
There are different types of Machine Learning (ML). These types of ML work in different ways and can be applied to different types of problem classes. What they have in common, however, is that a computer system learns on the basis of data. A distinction is made between supervised learning, non-supervised learning and reinforcement learning (see figure 1). 
 
Figure 1: Type of Learning and types of problems they address
2.1.	Supervised Learning
The spam detector is an example for supervised learning: It automatically learn a model of the relationship between a set of descriptive features and a target feature from a set of historical examples. The historical examples are mails with the already known spam property. We call these data also labelled data. The characteristics of supervised learning are thus a target variable that needs to be predicted, and labelled data with the target variable known, that are used to train the model. Supervised learning works in two phases (see figure 2): 
 
Figure 2: The process of supervised learning
In the training phase we apply a ML algorithm to labelled data (“the training data”) to learn a model from the descriptive features. A model is basically something that uses a set of features as input and predicts a value for the target variable. Examples for models are regression functions or artificial neural networks and many more. The type of model depends on the ML algorithm that is applied to the data. We will learn more about ML algorithms and the details of the training process in the following units of this module. 
In the prediction phase we use the trained model and apply it to new, unlabelled data to predict the target variable. A prediction is always associated with uncertainty. A good model has less uncertainty than a very accurate model. It is important to know the uncertainty of a model. If the model predicts a discrete target variable (only defined values possible, c.f. spam example), we talk about a classification problem. If the target variable is continuous, it is a regression problem. Examples for classification problems: 
•	Object detection on images: Is there a cat in the picture or not?
•	Character recognition: Identify the letters in a handwritten document
•	Will a customer churn or not?
Examples for regression problems:
•	Prediction of sales based on various factors
•	Prediction of expected revenue with a customer
2.2.	Unsupervised Learning
In contrast to supervised learning, unsupervised learning does not have a target variable that needs to be predicted. Therefore there is also no training phase and no model and no labelled data. Unsupervised learning methods try to learn patterns in the data. One important type of problems is to find clusters in a dataset: All data that are member of the same cluster are similar (whatever this means!), and they are different from the members of other clusters. In general it is not known how many cluster there are in a dataset. Maybe there are no clusters at all. An example for clustering is image clustering. Think of many photos that are all displaying flowers. Clustering of these images would try to group the photos in natural groups. 
Another type of problems that can be addressed with unsupervised learning is anomaly detection. As the name indicates, the ML algorithm tries to identify a situation in a system that is an outlier or not normal. An example is a defect in a technical system that occurs very rare or a hacker attack on a computer network.
2.3.	Reinforcement Learning
The latest type of ML is called reinforcement learning (RL). An agent independently learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties for its actions, and uses this feedback to improve over. RL is particularly useful for problems where the optimal solution is not known in advance and must be discovered through trial and error, such as in game playing, robotics, and control systems. RL algorithms are used in a wide range of applications such as gaming, finance, and autonomous systems. During the training runs, the data is generated and labelled. Figure 3 schematically displays RL:
 
Figure 3: Basic idea of reinforcement larning (RL)
Example: Think of a strategy game like Tic-Tac-Toe. The agent tries a strategy and gets feedback: Winning is positive, loosing is negative. A famous example for RL is AlphaGo by Google Deepmind : This system learned the very complicated Go game and won over the actual world champion. RL training is often done in a simulation environment, not in the real world! It is also possible to use human feedback as reward function . Many of the spectacular successes of AI in recent years use RL."