lecture_4.pptx

‫األكاديمية الليبية للدراسات‬ ‫العليا‬ ‫فرع بنغازي‬ ‫‪Artificial Intelligence‬‬ ‫‪Lecture 4 : Machine Learning in AI‬‬ ‫دكتور ‪.‬محمد لعبيدي‬ Introduction of Machine Learning Machine Learning Algorithms Categories of Machine Learning  Mac...

‫األكاديمية الليبية للدراسات‬ ‫العليا‬ ‫فرع بنغازي‬ ‫‪Artificial Intelligence‬‬ ‫‪Lecture 4 : Machine Learning in AI‬‬ ‫دكتور ‪.‬محمد لعبيدي‬ Introduction of Machine Learning Machine Learning Algorithms Categories of Machine Learning  Machine Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.  Machine Learning Algorithms They are several types of Algorithms in Machine Learning (ML). If you are aware of these Algorithms then you can use them well to apply in almost any data problems. Data Scientists and machine learning enthusiasts use these Algorithms to create various Functional. : The list of Machine Learning Algorithms 1.Linear Regression 2.Logistic Regression 3.Support Vector Machines 4.Random Forest 5.Naïve Bayes Classification 6.Ordinary Least Square Regression 7.K-means 8.Ensemble Methods 9.A priori Algorithm 10.Principal Component Analysis 11.Singular Value Decomposition 12.Reinforcement or Semi-Supervised Machine Learning 13.Independent Component Analysis  Categories of Machine Learning ML has three types of machine learning techniques, or categories, that are used to implement machine learning algorithms. Those are the categories: 1- Supervised Learning 2- Unsupervised Learning 3- Reinforcement Learning  Supervised Learning Supervised learning provides you with a powerful tool to classify and process data using machine language. Supervised learning uses labelled data, which is a data set that has been classified, to infer a learning algorithm. The data set is used as the basis for predicting the classification of other unlabelled data through the use of machine learning algorithms. Supervised learning uses classification algorithms and regression techniques to develop predictive models. The algorithms include Linear regression, Logistic Regression, and Neural Networks, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K-nearest Neighbour.  Supervised Learning Example of Supervised Learning Algorithms  Unsupervised Learning Under unsupervised learning there is no teacher and no student. It’s like self-study. Algorithm has to learn from the data set , how and on what basis it read the data set and make sense of it. For example, we can put a bucket full of fruits in machine and expect it to make sense of all the different fruits in it on the basis of shape, size, colour and structure. The algorithms are used for K-means for clustering problems, Apriori Algorithm for Association Rule Learning Problems, Principal Component Analysis , Singular Value Decomposition and Independent Component Analysis. The Processing of Unsupervised Learning Algorithms Examples of Supervised and Unsupervised Algorithms  Reinforcement Learning Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. Though both supervised and reinforcement learning use mapping between input and output, unlike supervised learning where feedback provided to the agent is correct set of actions for performing a task, reinforcement learning uses rewards and punishment as signals for positive and negative behaviour.. In Reinforcement Learning an agent decides the best action based on the current state of the results. Below graphics explains a lot about the reinforcement learning algorithms working. The Processing of Reinforcement Algorithms Example of Reinforcement Algorithms