Machine Learning Lec 3 | Waheed Anwer

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Computer Science Machine Learning Types Dr. Waheed Anwar Computer Science Types of Machine learning Computer Science Types of Machine learning Computer Science Types of Machine learning Problems Computer Science Supervised Learning Computer Science • Regression analysis is a statistical method to model the relationship between a dependent (target) and independent (predictor) variables with one or more independent variables. • More specifically, Regression analysis helps us to understand how the value of the dependent variable is changing corresponding to an independent variable when other independent variables are held fixed. It predicts continuous/real values such as temperature, age, salary, price, etc Regression Analysis Computer Science Classification algorithms are used to solve the classification problems in which the output variable is categorical, such as "Yes" or No, Male or Female, Red or Blue, etc. The classification algorithms predict the categories present in the dataset. Some real-world examples of classification algorithms are Spam Detection, Email filtering, etc. • Some popular classification algorithms are given below: o Random Forest Algorithm o Decision Tree Algorithm o Logistic Regression Algorithm o Support Vector Machine Algorithm Classification Computer Science Unsupervised Learning Computer Science The clustering technique is used when we want to find the inherent groups from the data. It is a way to group the objects into a cluster such that the objects with the most similarities remain in one group and have fewer or no similarities with the objects of other groups. An example of the clustering algorithm is grouping the customers by their purchasing behaviour. • Some of the popular clustering algorithms are given below: o K-Means Clustering algorithm o Mean-shift algorithm o DBSCAN Algorithm o Principal Component Analysis o Independent Component Analysis Clustering Computer Science Association rule learning is an unsupervised learning technique, which finds interesting relations among variables within a large dataset. The main aim of this learning algorithm is to find the dependency of one data item on another data item and map those variables accordingly so that it can generate maximum profit. This algorithm is mainly applied in Market Basket analysis, Web usage mining, continuous production, etc. • Some popular algorithms of Association rule learning are Apriori Algorithm, FP-growth algorithm. Association Computer Science Supervised Learning Unsupervised Learning 1. Supervised learning algorithms are trained using labeled data. Unsupervised learning algorithms are trained using unlabeled data. 2. Supervised learning model takes direct feedback to check if it is predicting correct output or not. Unsupervised learning model does not take any feedback. 3. Supervised learning model predicts the output. Unsupervised learning model finds the hidden patterns in data. 4. In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model. 5. The goal of supervised learning is to train the model so that it can predict the output when it is given new data. The goal of unsupervised learning is to find the hidden patterns and useful insights from the unknown dataset. 6. Supervised learning needs supervision to train the model. Unsupervised learning does not need any supervision to train the model. 7. Supervised learning can be categorized in Classification and Regression problems. Unsupervised Learning can be classified in Clustering and Associations problems. 8. Supervised learning can be used for those cases where we know the input as well as corresponding outputs. Unsupervised learning can be used for those cases where we have only input data and no corresponding output data. 9. Supervised learning model produces an accurate result. Unsupervised learning model may give less accurate result as compared to supervised learning. 10 Supervised learning is not close to true Artificial intelligence as in this, we first train the model for each data, and then only it can predict the correct output. Unsupervised learning is more close to the true Artificial Intelligence as it learns similarly as a child learns daily routine things by his experiences. 11 It includes various algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic, etc. It includes various algorithms such as Clustering, KNN, and Apriori algorithm. Supervised Learning vs Unsupervised Learning Computer Science Reinforcement Learning Computer Science o Positive Reinforcement Learning: Positive reinforcement learning specifies increasing the tendency that the required behaviour would occur again by adding something. It enhances the strength of the behaviour of the agent and positively impacts it. o Negative Reinforcement Learning: Negative reinforcement learning works exactly opposite to the positive RL. It increases the tendency that the specific behaviour would occur again by avoiding the negative condition. Categories of Reinforcement Learning Computer Science Type of Data in ML Types Computer Science Approach Computer Science Output Feedback Computer Science Popular Algorithms Computer Science Applications Computer Science Use case no 1 Computer Science Use Case no 2 Computer Science Use Case no 3 Computer Science Use Case no 4 Computer Science Use Case no 5