TAILIEUCHUNG - Book Machine Learning

The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. In recent years many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to information-filtering systems that learn users' reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundations of this field | M M. MITCH1 Machine Learning Tom M. Mitchell Product Details Hardcover 432 pages Dimensions in inches x x Publisher McGraw-Hill Science Engineering Math March 1 1997 ISBN 0070428077 Average Customer Review Airfrtu Based on 16 reviews. Sales Rank 42 816 Popular in Redmond WA 17 Ithaca NY 9 Editorial Reviews From Book News Inc. An introductory text on primary approaches to machine learning and the study of computer algorithms that improve automatically through experience. Introduce basics concepts from statistics artificial intelligence information theory and other disciplines as need arises with balanced coverage of theory and practice and presents major algorithms with illustrations of their use. Includes chapter exercises. Online data sets and implementations of several algorithms are available on a Web site. No prior background in artificial intelligence or statistics is assumed. For advanced undergraduates and graduate students in computer science engineering statistics and social sciences as well as software professionals. Book News Inc. Portland OR Book Info Presents the key algorithms and theory that form the core of machine learning. Discusses such theoretical issues as How does learning performance vary with the number of training examples presented and Which learning algorithms are most appropriate for various types of learning tasks DLC Computer algorithms. Book Description This book covers the field of machine learning which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning PREFACE The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. In recent years many successful machine learning applications have been developed ranging from data-mining programs that learn to detect .

• Phần cứng
• Machine Learning
• computer programs
• datamining programs
• autonomous vehicles
• information theory
• control theory
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Max margin learning
• Multi instance learning
• Reinforcement learning
• Delayed rewards
• Machine Learning Introduction
• Machine Learning Work
• Types of Machine Learning
• Supervised Learning
• Forms of Supervised Learning
• Bayesian Learning
• Back propagation
• Boltzmann machines
• Linear prediction
• Linear regression models
• Maximum likelihood
• Gaussian distributions
• Nonlinear ridge regression risk
• Cross validation
• Newton’s algorithm
• Gradient descent algorithm
• Logistic regression
• Logistic regression likelihood
• Formulate layers
• Neural networks
• Artificial neural networks
• Convolutional neural networks
• Design convolutional layers
• Recurrent nets
• Recurrent neural networks
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Linear regression
• Learning a regression function
• Unsupervised learning
• Partition based clustering
• The sewing machine classroom
• Learning the ins
• Outs of your machine
• Mastering the materials
• Demystifying your machine
• Increasing your sphere
• Deep Belief Network
• Restricted Boltzmann Machine
• Support Vector Machine
• Automatic CNC program
• Challenges in Machine Learning
• Data Mining
• Learning from non vectorial data
• Beyond classification and regression
• Distributed data mining
• Machine learning bottleneck
• Introduction to machine learning
• Bayesian decision theory
• Parametric methods
• Multivariate methods
• BMC Bioinformatics
• Extreme learning machine
• Correntropy induced loss
• Machine learning method
• K nearest neighbors
• Neighbor based learnin
• Decision tree
• Random forest
• Support vector machines
• Separating hyperplane
• Artificial neural network
• Rectified linear unit
• Normalization methods
• Deep learning
• Representation learning
• Phenotype prediction
• Molecular diagnostics