Learning Method In Data Mining

Nice and in simple english easy to present breifly described every topics with there algorithms and examples.

1. Learning Methods Group Members M.Ishaq ZAMAN Arslan NaziR Bilal Latif

2. What Is Learning? Process of learning begins with observation of data such as, examples, direct experience or instructions. Its aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

3. Learning Methods Are.. Supervised Machine learning algorithms Unsupervised Machine learning algorithms Semi-supervised Machine learning algorithms Reinforcement Machine learning algorithms

4. Supervised machine Learning C an apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. Algorithm analyzes the training data set and produces an inferred function. If the output of the function is discrete than it is called classifier and if the output is continuous than it is called a regression function.

5. Example If the inputs are 1,2,3,4,5,6 and the outputs according to the inputs are 1,4,9,16,25,36 Then we can predict the next output by the help of function which we get from above which is output=input^2 So if the next input is 7 than by putting in function the output will be 49

6. Supervised learning algorithm Support V ector M achine Algorithm (SVM)

7. Unsupervised Machine learning No labels are given to the learning algorithm, leaving it on its own to find structure in its inputs  Unsupervised learning can be a goal in itself (discovering hidden patterns in data ). The data have no target attribute.

8. Example You have bunch of photos of 6 people but  without information who is on which one  and want to  divide  this dataset into 6 piles, each with photos of one individual.

9. Unsupervised learning algorithm K – means clustering

10. Semi-supervised Learning Semi-supervised learning falls in between Supervised and Unsupervised. Semi-supervised learning use small amount of labeled data and large amount of unlabeled data. The goal is to learn a predictor that predicts future test data better than the predictor learned from the labeled training data alone.  This for example can be used in Deep belief networks, where some layers are learning the structure of the data (unsupervised) and one layer is used to make the classification (trained with supervised data)

11. Reinforcement machine learning algorithms  I s a learning method that interacts with its environment by producing actions and discovers errors or rewards.  Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states . This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance

12. Steps Reinforcement algo work.. In order to produce intelligent programs (also called agents), reinforcement learning goes through the following steps: Input state is observed by the agent. Decision making function is used to make the agent perform an action. After the action is performed, the agent receives reward or reinforcement from the environment. The state-action pair information about the reward is stored.

13. Architecture

14. Reinforcement algo.. Q-learning Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)]