Machine Learning techniques used in AI.
- 1. MACHINE LEARNING TECHNIQUES IN ARTIFICIAL INTELLIGENCE - T.ARCHANA ASSISTANT PROFESSOR COMPUTER SCIENCE AND ENGINEERING SRM INSTITUTE OF SCIENCE AND TECHNOLOGY
- 2. AGENDA • About AI • AI vs ML vs DL • Machine learning • Types of machine learning • Supervised learning • Unsupervised Learning • Reinforcement Learning • Algorithms used in ML • Use cases of ML
- 3. ABOUT AI • AI is a technique that enables machines to mimic human behavior. Artificial Intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence.
- 4. WHY ARTIFICIAL INTELLIGENCE? • With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc. • With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc. • With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk. • AI opens a path for other new technologies, new devices, and new Opportunities.
- 6. AI VS ML VS DL • AI- Artificial Intelligence is defined as a field of science and engineering that deals with making intelligent machines or computers to perform human-like activities. • ML- Machine Learning is defined as the branch of Artificial Intelligence and computer science that focuses on learning and improving the performance of computers/machines through past experience by using algorithms. • DL - Deep Learning is a set of algorithms inspired by the structure and function of the human brain. It uses a huge amount of structured as well as unstructured data to teach computers and predicts accurate results.
- 8. MACHINE LEARNING • Subset of AI provides the machine to learn automatically and improve from experience without being explicitly programmed • A Machine Learning algorithm is a set of rules and statistical techniques used to learn patterns from data and draw significant information from it.
- 9. EXAMPLE
- 10. TYPES OF MACHINE LEARNING
- 11. SUPERVISED LEARNING • Supervised learning is a method in which we teach the machine using labelled data. • Supervised Learning is the process of making an algorithm to learn to map an input to a particular output. This is achieved using the labelled datasets that you have collected. • If the mapping is correct, the algorithm has successfully learned.
- 15. UNSUPERVISED LEARNING • The machine is trained on unlabelled data without any guidance • The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format.
- 19. REINFORCEMENT LEARNING • An agent interacts with its environment by producing actions and discovers errors and rewards • Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty. • The agent learns with the process of hit and trial, and based on the experience
- 21. PROBLEM AIM ALGORITHMS APPLICATIONS TYPE
- 22. RISKS OF MACHINE LEARNING • Poor Data • Overfitting • Biased data • Lack of strategy and experience • Security Risks • Data privacy and confidentiality • Third-party risks • Regulatory challenges
- 23. ALGORITHMS USED IN ML SUPERVISED LEARNING Linear Regression Logistic Regression SVM K- Nearest Neighbour Random Forest Decision Tree UNSUPERVISED LEARNING K- Means Apriori C- Means REINFORCEMENT LEARNING Q- Learning SARSA (State Action Reward State Action)
- 24. LINEAR REGRESSION • It is a statistical method that is used for predictive analysis. • Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc. • Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (y) variables, hence called as linear regression. • The linear regression model provides a sloped straight line representing the relationship between the variables. • It finds how the value of the dependent variable is changing according to the value of the independent variable.
- 25. Mathematically, we can represent a linear regression as: y= a0+a1x+ ε Here, Y= Dependent Variable (Target Variable) X= Independent Variable (predictor Variable) a0= intercept of the line (Gives an additional degree of freedom) a1 = Linear regression coefficient (scale factor to each input value). ε = random error The values for x and y variables are training datasets for Linear Regression model representation.
- 26. LINEAR REGRESSION USE CASES • Sales Forecasting • Risk Analysis • Housing Applications To Predict the prices and other factors • Finance Applications To Predict Stock prices, investment evaluation, etc.
- 27. LOGISTIC REGRESSION • Logistic Regression analysis predicts the outcome in a binary variable which has only two possible outcomes. • It is a technique to analyse a data-set which has a dependent variable and one or more independent variables to predict the outcome in a binary variable, meaning it will have only two outcomes. • The dependent variable is categorical in nature.
- 28. Linear regression equation: y = β0 + β1X1 + β2X2 …. + βnXn •Y stands for the dependent variable that needs to be predicted. •β0 is the Y-intercept, which is basically the point on the line which touches the y-axis. •β1 is the slope of the line (the slope can be negative or positive depending on the relationship between the dependent variable and the independent variable.) •X here represents the independent variable that is used to predict our resultant dependent value.
- 29. LOGISTIC REGRESSION USE CASES • Weather Prediction • Determining Illness
- 30. SVM • It is used for Classification problems in Machine Learning. • The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. • SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
- 31. To select the maximum hyperplane in the given sets, the support vector machine follows the following sets: •Generate hyperplanes which segregates the classes in the best possible way •Select the right hyperplane with the maximum segregation from either nearest data points
- 32. SUPPORT VECTOR MACHINE USE CASES • Face Detection • Text And HyperText Categorization • Classification Of Images • Bioinformatics • Protein Fold and Remote Homology Detection • Handwriting Recognition • Generalized Predictive Control
- 33. KNN • K nearest neighbors or KNN Algorithm is a simple algorithm which uses the entire dataset in its training phase. Whenever a prediction is required for an unseen data instance, it searches through the entire training dataset for k-most similar instances and the data with the most similar instance is finally returned as the prediction. • K-NN algorithm stores all the available data and classifies a new data point based on the similarity.
- 34. How to Choose the K- value? • You can use cross validation technique to test several values of k and choose the best or which suits the best. • The distance is measeured using • Euclidean distance • Manhattan distance
- 35. KNN USE CASES • Amazon – Recommed Systems • Concept Search
- 36. RANDOM FOREST • The algorithm randomly creates a forest with several trees. • The higher the number of trees in the forest, greater is the accuracy of the results. • Random forest builds multiple decision trees (called the forest) and glues them together to get a more accurate and stable prediction. The forest it builds is a collection of Decision Trees, trained with the bagging method.
- 37. The dataset is divided into subsets and given to each decision tree. During the training phase, each decision tree produces a prediction result, and when a new data point occurs, then based on the majority of results, the Random Forest classifier predicts the final decision.
- 39. RANDOM FOREST USE CASES • Banking: Banking sector mostly uses this algorithm for the identification of loan risk. • Medicine: With the help of this algorithm, disease trends and risks of the disease can be identified. • Land Use: We can identify the areas of similar land use by this algorithm. • Marketing: Marketing trends can be identified using this algorithm.
- 40. K-MEANS CLUSTERING • k-means clustering is one of the simplest algorithms which uses unsupervised learning method to solve known clustering issues. k-means clustering require following two inputs. 1.k = number of clusters 2.Training set(m) = {x1, x2, x3,……….., xm}
- 41. HOW DOES THE K- MEANS ALGORITHM WORK? Step-1: Select the number K to decide the number of clusters. Step-2: Select random K points or centroids. (It can be other from the input dataset). Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters. Step-4: Calculate the variance and place a new centroid of each cluster. Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster. Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.
- 42. K-MEANS CLUSTERING USE CASES • Customer Segmentation • Identifying Crime Localities • Document Classification • Diagnostic systems • Search engines • Wireless sensor networks
- 43. APRIORI ALGORITHM • The Apriori algorithm uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected. • Frequent itemsets are those items whose support is greater than the threshold value or user-specified minimum support.
- 44. • Support: It gives the fraction of transactions which contains item A and B. Basically Support tells us about the frequently bought items or the combination of items bought frequently. • Confidence: It tells us how often the items A and B occur together, given the number times A occurs.
- 45. Step-1: Determine the support of itemsets in the transactional database, and select the minimum support and confidence. Step-2: Take all supports in the transaction with higher support value than the minimum or selected support value. Step-3: Find all the rules of these subsets that have higher confidence value than the threshold or minimum confidence. Step-4: Sort the rules as the decreasing order
- 46. APRIORI ALGORITHM USE CASES • Education Field: Extracting association rules in data mining of admitted students through characteristics and specialties. • Medical field: For example Analysis of the patient’s database. • Forestry: Analysis of probability and intensity of forest fire with the forest fire data. • Apriori is used by many companies like Amazon in the Recommender System and by Google for the auto-complete feature.
- 47. USE CASES OF ML
- 48. USE CASES OF ML
- 49. USE CASE- IMAGE & VIDEO RECOGNITION Companies using image & video recognition: Google, Shutterstock, Pinterest, eBay, Salesforce, Yelp, Apple, Amazon, Facebook.
- 50. USE CASE- SPEECH RECOGNITION • Speech recognition is used in search engines (e.g. Google, Baidu), virtual digital assistants (i.e. Alexa, Cortana, Siri, Google Assistant, AliGenie), smart speakers (e.g. Amazon Echo, Google Home), and voice-activated applications (e.g. Uber, Evernote).
- 51. USE CASE- HEALTH CARE • Machine Learning for healthcare and bioinformatics can process a massive amount of data and deliver valuable insights that can help healthcare professionals in making quick decisions. • It also allows them to analyze a patient’s medical history and predict the outcomes based on their treatment and lifestyle.
- 52. USE CASE - FINANCE • Machine Learning is being used in the finance industry to make businesses automated and more secure. • It is used for Financial Monitoring ,Process automation, Secure transaction, Risk management, Algorithmic trading, Financial regulators and advisory, Customer data management, Decision making and investment prediction, Customer service improvement, Customer retention program, Marketing