Lecture17
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This document provides an overview of clustering as presented in a machine learning lecture. It defines clustering as separating unlabeled data into natural groups. It discusses what makes a good clustering solution, including homogeneity within clusters and separation between clusters. The document also covers hierarchical clustering as a type of clustering, provides an example and pseudocode, and discusses distance functions and limitations of hierarchical clustering algorithms.
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machine learning
machine learningRaheemUnnisa1 Machine learning is a scientific discipline that develops algorithms to allow systems to learn from data and improve automatically without being explicitly programmed. The document discusses several key machine learning concepts including supervised learning algorithms like decision trees and Naive Bayes classification. Decision trees use branching to represent classification or regression rules learned from data to make predictions. Naive Bayes classification is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between features.
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Lecture1 AI1 Introduction to artificial intelligenceAlbert Orriols-Puig This document provides an overview of the history and development of artificial intelligence, beginning with early concepts of artificial beings and progressing through milestones like the Dartmouth Conference, development of expert systems in the 1970s-80s, advances in the 1990s with things like Deep Blue and robotics, and examples of modern applications like robotic vacuums and self-driving cars through challenges like DARPA's Grand Challenge.
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Lecture22Albert Orriols-Puig This document summarizes a lecture on reinforcement learning and the Q-learning algorithm. Q-learning is a temporal difference learning method that allows an agent to learn optimal policies without needing a model of the environment's dynamics. The algorithm works by learning an action-value function (Q-function) that directly approximates the optimal Q-function through Q-backups without requiring a model of the environment. Pseudocode is provided for the basic Q-learning algorithm. Examples are also given showing how Q-learning can be used to learn an optimal policy for navigating a maze.
Lecture14 - Advanced topics in association rules
Lecture14 - Advanced topics in association rulesAlbert Orriols-Puig This document summarizes lecture 14 on advanced topics in association rule mining. It begins with a recap of lecture 13 and the challenges of Apriori, including its multiple database scans and large number of candidate itemsets generated. It then introduces FP-growth as an alternative method that avoids candidate generation by building an FP-tree structure from the database in one scan. The document explains how to build the FP-tree and then mine it to extract frequent patterns directly without candidate generation. It notes that FP-growth scales better than Apriori as the support threshold decreases since it avoids costly candidate generation and testing.
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Lecture12 - SVM
Lecture12 - SVMAlbert Orriols-Puig This document provides a summary of a lecture on support vector machines (SVMs). The lecture discusses how SVMs find the optimal separating hyperplane between two classes by maximizing the margin between them. It covers both the separable and non-separable cases, and how SVMs can be extended to non-linear classification using kernel tricks. The lecture concludes by mentioning further issues like multi-class classification and algorithms for building SVMs.
Lecture10 - Naïve Bayes
Lecture10 - Naïve BayesAlbert Orriols-Puig This document provides a summary of Lecture 10 on Bayesian decision theory and Naive Bayes machine learning algorithms. It begins with a recap of Lecture 9 on using probability to classify patterns into categories. It then discusses how to apply these probabilistic concepts to both nominal and continuous variables. A medical example is presented to illustrate Bayesian classification. The document concludes by explaining the Naive Bayes algorithm for classification tasks and providing a worked example of how it is trained and makes predictions.
HIS'2008: Artificial Data Sets based on Knowledge Generators: Analysis of Lea...
HIS'2008: Artificial Data Sets based on Knowledge Generators: Analysis of Lea...Albert Orriols-Puig The document proposes a framework for evaluating machine learning algorithms using artificially generated data sets based on predefined knowledge. It defines a methodology that involves sampling problems, generating data sets using different sampling methods and sizes, and running learning algorithms with varying parameters. Results are analyzed by plotting accuracy and optimal population over iterations to compare algorithm efficiency under different conditions. The methodology allows reducing the number of iterations needed for evaluation.
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