Machine Learning guide for beginners
What is machine learning?
Machine learning is a powerful new tool for solving problems, from filtering a photo collection to helping people tackle some of the world’s most pressing global challenges in health, environment, and beyond. In these videos, we’ll explore what these technologies are and how they can be applied in real life to help businesses grow.
Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT systems to independently find solutions to problems by recognizing patterns in databases. In other words: Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms and data sets and to develop adequate solution concepts. Therefore, in Machine Learning, artificial knowledge is generated on the basis of experience.
In order to enable the software to independently generate solutions, the prior action of people is necessary. For example, the required algorithms and data must be fed into the systems in advance and the respective analysis rules for the recognition of patterns in the data stock must be defined. Once these two steps have been completed, the system can perform the following tasks by Machine Learning:
- Finding, extracting and summarizing relevant data
- Making predictions based on the analysis data
- Calculating probabilities for specific results
- Adapting to certain developments autonomously
- Optimizing processes based on recognized patterns
How it works
In a way, Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learning works in the same way: Through data input and certain commands, the computer is enabled to “learn” to identify certain objects (persons, objects, etc.) and to distinguish between them. For this purpose, the software is supplied with data and trained. For instance, the programmer can tell the system that a particular object is a human being (=”human”) and another object is not a human being (=”no human”). The software receives continuous feedback from the programmer. These feedback signals are used by the algorithm to adapt and optimize the model. With each new data set fed into the system, the model is further optimized so that it can clearly distinguish between “humans” and “non-humans” in the end.
But Machine Learning means much more than just distinguishing between two classes. Using the KUKA table tennis robot as an example, you can see how a machine scans the complex tendencies and the playing style of its opponent, adapts to them and even makes a world champion sweat this way.
Algorithms learn from data
They find relationships, develop understanding, make decisions, and evaluate their confidence from the training data they’re given. And the better the training data is, the better the model performs.
Choosing the right algorithm can seem overwhelming — there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.
There is no best method or one size fits all. Finding the right algorithm is partly just trial and error — even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.
Types of Machine Learning
Basically, algorithms play an important role in Machine Learning: On the one hand, they are responsible for recognizing patterns and on the other hand, they can generate solutions. Algorithms can be divided into different categories:
Supervised learning: In the course of monitored learning, example models are defined in advance. In order to ensure an adequate allocation of the information to the respective model groups of the algorithms, these then have to be specified. In other words, the system learns on the basis of given input and output pairs. In the course of monitored learning, a programmer, who acts as a kind of teacher, provides the appropriate values for a particular input. The aim is to train the system in the context of successive calculations with different inputs and outputs and to establish connections.
Unsupervised learning:In unsupervised learning, artificial intelligence learns without predefined target values and without rewards. It is mainly used for learning segmentation (clustering). The machine tries to structure and sort the data entered according to certain characteristics. For example, a machine could (very simply) learn that coins of different colours can be sorted according to the characteristic “colour” in order to structure them.
Partially supervised learning:Partially supervised learning is a combination of supervised and unsupervised learning.
Encouraging learning: Reinforcing learning — just like Skinner’s classic conditioning — is based on rewards and punishments. The algorithm is taught by a positive or negative interaction which reaction to a certain situation should take place.
Active learning: Within the framework of active learning, an algorithm is given the opportunity to query results for specific input data on the basis of pre-defined questions that are considered significant. Usually, the algorithm itself selects questions with high relevance.
In general, the data basis can be either offline or online, depending on the corresponding system. In addition, it can be available only once or repeatedly for Machine Learning. Another distinguishing feature is the either staggered development of the input and output pairs or their simultaneous presence. On the basis of this aspect, a distinction is made between so-called sequential learning and so-called batch learning.
Advantages of Machine Learning
Machine Learning undoubtedly helps people to work more creatively and efficiently. Basically, you too can delegate quite complex or monotonous work to the computer through Machine Learning — starting with scanning, saving and filing paper documents such as invoices up to organizing and editing images.
In addition to these rather simple tasks, self-learning machines can also perform complex tasks. These include, for example, the recognition of error patterns. This is a major advantage, especially in areas such as the manufacturing industry: the industry relies on continuous and error-free production. While even experts often cannot be sure where and by which correlation a production error in a plant fleet arises, Machine Learning offers the possibility to identify the error early — this saves downtimes and money.
Self-learning programs are now also used in the medical field. In the future, after “consuming” huge amounts of data (medical publications, studies, etc.), apps will be able to warn a in case his doctor wants to prescribe a drug that he cannot tolerate. This “knowledge” also means that the app can propose alternative options which for example also take into account the genetic requirements of the respective patient.
What is the difference between training data and big data?
Big data and training data are not the same thing. Gartner calls big data “high-volume, high-velocity, and/or high-variety” and this information generally needs to be processed in some way for it to be truly useful. Training data, as mentioned above, is labeled data used to teach AI models or machine learning algorithms.
List of Common Algorithms:
- Nearest Neighbor
- Naive Bayes
- Decision Trees
- Linear Regression
- Support Vector Machines (SVM)
- Neural Networks
ARTIFICIAL INTELLIGENCE — TWO TYPES
The idea of artificial intelligence is relatively familiar to many, but popular representations are actually misleading. In practice, there exist two different types of A.I. — strong and narrow.
Machines that learn language more like kids do
Children learn language by observing their environment, listening to the people around them, and connecting the dots between what they see and hear. Among other things, this helps children establish their language’s word order, such as where subjects and verbs fall in a sentence.
In computing, learning language is the task of syntactic and semantic parsers. These systems are trained on sentences annotated by humans that describe the structure and meaning behind words. Parsers are becoming increasingly important for web searches, natural-language database querying, and voice-recognition systems such as Alexa and Siri. Soon, they may also be used for home robotics.
But gathering the annotation data can be time-consuming and difficult for less common languages. Additionally, humans don’t always agree on the annotations, and the annotations themselves may not accurately reflect how people naturally speak.
In a paper being presented at this week’s Empirical Methods in Natural Language Processing conference, MIT researchers describe a parser that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend the parser’s capabilities. To learn the structure of language, the parser observes captioned videos, with no other information, and associates the words with recorded objects and actions. Given a new sentence, the parser can then use what it’s learned about the structure of the language to accurately predict a sentence’s meaning, without the video.
Training data:
Neural networks and other artificial intelligence programs require an initial set of data, called training data, to act as a baseline for further application and utilization. This data is the foundation for the program’s growing library of information.
Why Machine Learning Matters?
With the rise in big data, machine learning has become a key technique for solving problems in areas, such as:
- Computational finance, for credit scoring and algorithmic trading
- Image processing and computer vision, for face recognition, motion detection, and object detection
- Computational biology, for tumor detection, drug discovery, and DNA sequencing
- Energy production, for price and load forecasting
- Automotive, aerospace, and manufacturing, for predictive maintenance
- Natural language processing, for voice recognition applications
Summary :
http://news.mit.edu/2018/machines-learn-language-human-interaction-1031
