Artificial Intelligence Approaches
- 2. AI Approaches • Ai models have two approaches: • Rule based(eg: decision Tree) • Learning based – Machine learning. – Deep Learning.
- 3. Rule Based Approaches • A rule based system uses rules as the knowledge representation . • The rules are coded into the system in the form of a series of if-then-else statement that guides a computer to reach a conclusion or recommendation.
- 5. • The rule based system s just fake intelligence because of the missing learning capability. • The rule based system is said to be limited in its ability to simulate intelligence. • It is always limited by the size of its underlying rule base.
- 6. • A rule based system is built on two main components: – A set of facts about a situation ( also known as knowledge base. Eg: these facts are a combination of data). – A set of rules for how to deal with those facts( also known as the rules engine which describe the relation between the IF and THEN statement.
- 7. Decision Tree • A decision tree is a series of nodes. • A directional graph that starts at the base with a single node and extends to the many leaf nodes that represent the categories that the tree can classify. • It looks like an up side down tree.
- 9. Components of decision tree • Question/Condition are Nodes or Roots. • Ye/No options represents Edge or Branches • End Actions are leaves of the tree.
- 10. • Advantages of decision tree: – Interpretable: – Any data can be used. • Disadvantages of Decision Tree: – Overfitting – Predicting continuous variable
- 12. Limitations of Rule based approach • It is tough to add rules to an already large knowledge base without introducing contradiction rules. • Maintenance Time consuming and expensive. • Not useful for solving problems in complex domains or across multiple simple domains. • Some times It is not possible to explicitly define rules in a programmatic or declarative way.
- 13. Learning Based Approach • The ability to learn causes adaptive intelligence. • Adaptive intelligence : Existing knowledge can be changed or discarded. • Hence , the system build its rule on the fly. • A neural network is an instance of a learning system.
- 14. • The decision whether to go for a rule- based system or learning based system depends on the problem you want to solve, and its always a trade-off among efficiency , training cost and understanding
- 15. Relation between A I, Machine Learning and Deep Learning
- 16. What is Machine Learning • ML is an application of AI • Provides the system the ability to automatically learn and improve from experience without being explicitly programmed. • ML focus on the development of computer programs that can access data and use it to learn.
- 17. • Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed. _Arthur Samuel(1959)
- 18. • Through machine learning computer can be trained to automate tasks that would be exhaustive or impossible for a human being. • ML enables people to performs tasks such as: • Predicting the future • Classifying things in a meaningful way. • Making rational decision in a given context.
- 19. Difference between AI And ML AI Concept of machines being able to carry out tasks in a SMART way Goal : simulate natural intelligence to solve complex problems Decision making Leads to develop a system to mimic human to respond in certain circumstances ML Application of AI based on the idea that machines access data and let them learn for themselves. Goal : learn from data ob certain tasks to maximize the performance of machine on the given task. Allows systems to learn from data It involve creating self learning algorithms
- 20. The Machine learning Models 1. Takes data and learns from what happened before(learn from experience). 2.It then predicts what’s going to come next. 3.Tries and improves to find new solutions.
- 21. Steps in a general Machine Learning model. 1. Define objective 2. Collect Data 3. Prepare data 4. Select the algorithm to be used 5. Train the model 6. Test the model 7. Predict the output 8. Deploy
- 23. Types of Machine Learning • Supervised Learning: is a method used to enable machines to classify/predict objects, problems or solutions based on labelled data fed into the machine • Un-supervised Learning: the learning model is handed an unlabelled dataset without explicit instructions on what to do with it. • Semi-supervised :It requires both supervised and unsupervised methods in order to obtain useful result • Reinforcement learning: the required value of the output is not known explicitly, but the system provides feedback on the provided output.
- 25. Supervised Learning: • In supervised learning we train an algorithm and at the end pick a model that best predict well-defined output based on the in put data. • In SL the system receives input and output in the beginning. • Then based on i/p and o/p it create appropriate rules to map the input to output.
- 27. Steps to solve a problem of supervised learning 1. Determine the type of training examples. 2. Gather a fully labelled training set. 3. Determine the input feature representation of the learned function. 4. Determine the structure of the learning function and learning algorithm. 5. Complete the design. 6. Evaluate the accuracy of the learning function.
- 28. List of common Algorithms • Nearest Neighbor • Naive Bayes • Decision Trees • Linear Regression • Support Vector Machine(SVM) • Neural Networks
- 29. Types of Supervised Learning • Classification: Classification separates data. The variable output is a category, such as “Red”, “Blue” or “animal” “bird” • Regression: a technique to reproduce the output value. The output variable is real value. such as “Dollar” or “weight”
- 33. Unsupervised Learning • The learning model is handed an unlabelled dataset without explicit instructions on what to do with it. • Then attempts are made to automatically find structure in the data by extracting useful features and analysing the structure.
- 36. • Depending on the problem ,the unsupervised learning model can handle data in different ways: • Clustering • Anomaly detection • Association rule
- 37. Clustering • A clustering problem is where you want to find the inherent groups in the data. • Clustering works on discrete dataset. • It is used to find similarities and differences. • Machine generates its own rules or algorithms to differentiate the given dataset.
- 39. Anomaly Detection • It is the identification of rare items , events or observations differing from the majority of the data. • Anomalous data can be connected to some kind of problem or rare events. • Eg: bank fraud, medical problems , malfunctioning equipments etc
- 42. Association rule • Is a procedure which aims to observe frequently occurring patterns , correlations, or associations from datasets found in various kinds of databases. • Eg: Market based analysis
- 44. Semi-Supervised learning: • Is a learning process in which lots of output values are missing(the one we want to predict). • It requires both supervised and unsupervised methods in order to obtain useful result
- 45. Reinforcement learning: • the required value of the output is not known explicitly, but the system provides feedback on the provided output. • As the agent takes action that goes towards the goal, it receives a reward.
- 47. Deep Learning • Deep leaning is a machine learning technique that teaches computer to do what comes naturally to human. • “Learn by Examples”. • Deep learning is a technique that mimics the network of neurons in a brain.
- 48. • Deep learning is a subset of machine learning. • The machine uses different layers to learn from the data. • The dept of the model is represented by the number of layers in the model. • The learning phase is done through neural network.
- 49. Neural Network • A neural network is an architecture where the layers are stacked on top of each other.
- 51. Deep Learning Vs Machine learning