4. Introduction to Supervised, Unsupervised and Reinforcement Learning

 

4. Introduction to Supervised, Unsupervised and Reinforcement Learning

 

ü  The amount of data generated in the world today is very huge. This data is generated not only by humans but also by smartphones, computers and other devices. Based on the kind of data available and a motive present, certainly, a programmer will choose how to train an algorithm using a specific learning model.

·       Machine Learning is a part of Computer Science where the efficiency of a system improves itself by repeatedly performing the tasks by using data instead of explicitly programmed by programmers. Further let us understand the difference between three techniques of Machine Learning- Supervised, Unsupervised and Reinforcement Learning.

 

How Machine Learning Works?

Types of Learning Algorithms

       i.          Supervised learning

     ii.          Unsupervised learning

   iii.          Reinforcement learning

Supervised Learning

·       In Supervised learning, an AI system is presented with data which is labeled, which means that each data tagged with the correct label.

·       The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

·       As shown in the example, we have initially taken some data and marked them as ‘Spam’ or ‘Not Spam’. This labeled data is used by the training supervised model, this data is used to train the model.

·       Once it is trained we can test our model by testing it with some test new mails and checking of the model is able to predict the right output.

        

Types of Supervised learning

       i.          Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.

     ii.          Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

 

Unsupervised Learning Algorithm

·       In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training.

·       In the example, we have given some characters to our model which are ‘Ducks’ and ‘Not Ducks’.

·       In our training data, we don’t provide any label to the corresponding data.

·       The unsupervised model is able to separate both the characters by looking at the type of data and models the underlying structure or distribution in the data to learn more about it.

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 Types of Unsupervised Learning

       i.          Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

     ii.          Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people who buy X also tend to buy Y.

 

 Reinforcement Learning

·       A reinforcement learning algorithm, or agent, learns by interacting with its environment.

·       The agent receives rewards by performing correctly and penalties for performing incorrectly.

·       The agent learns without intervention from a human by maximizing its reward and minimizing its penalty.

·       It is a type of dynamic programming that trains algorithms using a system of reward and punishment.

 

In the example, we can see that the agent is given 2 options i.e. a path with water or a path with fire.

·       A reinforcement algorithm works on reward a system i.e. if the agent uses the fire path then the rewards are subtracted and agent tries to learn that it should avoid the fire path.

·       If it had chosen the water path or the safe path then some points would have been added to the reward points, the agent then would try to learn what path is safe and what path isn’t.

·       It is basically leveraging the rewards obtained, the agent improves its environment knowledge to select the next action.

 

 

Applications of Machine Learning

•Recognizing patterns:

ü  Facial identities or facial expressions

ü  Handwritten or spoken words

ü  Medical images

•Generating patterns:

ü  Generating images or motion sequences

•Recognizing anomalies:

ü  Unusual sequences of credit card transactions

ü  Unusual patterns of sensor readings in a nuclear power plant or unusual sound in your car engine.

•Prediction:

ü  Future stock prices or currency exchange rates

 

Influence of Disciplines on Machine Learning

       i.          Artificial intelligence

Learning symbolic representations of concepts. Machine learning as a search problem. Learning as an approach to improving problem solving. Using prior knowledge together with training data to guide learning.

     ii.          Bayesian methods

Bayes' theorem as the basis for calculating probabilities of hypotheses. The naive Bayes classifier. Algorithms for estimating values of unobserved variables.

   iii.          Computational complexity theory

Theoretical bounds on the inherent complexity of different learning tasks, measured in terms of the computational effort, number of training examples, number of mistakes, etc. required in order to learn.

   iv.          Control theory

Procedures that learn to control processes in order to optimize predefined objectives and that learn to predict the next state of the process they are controlling.

     v.          Information theory

Measures of entropy and information content. Minimum description length approaches to learning. Optimal codes and their relationship to optimal training sequences for encoding a hypothesis.

   vi.          Philosophy

Occam's razor, suggesting that the simplest hypothesis is the best. Analysis of the justification for generalizing beyond observed data.

  vii.          Psychology and neurobiology

The power law of practice, which states that over a very broad range of learning problems, people's response time improves with practice according to a power law. Neurobiological studies motivating artificial neural network models of learning.

viii.          Statistics

Characterization of errors (e.g., bias and variance) that occur when estimating the accuracy of a hypothesis based on a limited sample of data. Confidence intervals, statistical tests.

 

 

	Supervised learning	Unsupervised learning	Reinforcement learning
Definition	Makes predictions from data	Segments and groups data	Reward-punishment system and interactive environment
Types of data	Labelled data	Unlabeled data	Acts according to a policy with a final goal to reach (No or predefined data)
Commercial value	High commercial and business value	Medium commercial and business value	Little commercial use yet
Types of problems	Regression and classification	Association and Clustering	Exploitation or Exploration
Supervision	Extra supervision	No	No supervision
Algorithms	Linear Regression, Logistic Regression, SVM, KNN and so forth	K – Means clustering,  C – Means, Apriori	Q – Learning,  SARSA
Aim	Calculate outcomes	Discover underlying patterns	Learn a series of action
Application	Risk Evaluation, Forecast Sales	Recommendation System, Anomaly Detection	Self-Driving Cars, Gaming, Healthcare

 

 

Which is the better Machine Learning technique?

ü  We learned about the three main members of the machine learning family essential for deep learning. Other kinds of learning are also available such as semi-supervised learning, or self-supervised learning.

ü  Supervised, unsupervised, and reinforcement learning, are all used for different to complete diverse kinds of tasks. No single algorithm exists that can solve every problem, as problems of different natures require different approaches to resolve them.

ü  Despite the many differences between the three types of learning, all of these can be used to build efficient and high-value machine learning and Artificial Intelligence applications. All techniques are used in different areas of research and development to help solve complex tasks and resolve challenges.