Machine learning (Record no. 14417)
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| 000 -LEADER | |
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| fixed length control field | 06322nam a22002057a 4500 |
| 005 - DATE & TIME | |
| control field | 20260218151116.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260218b |||||||| |||| 00| 0 eng d |
| 020 ## - ISBN | |
| International Standard Book Number | 9789393330697 |
| Price | 850.00 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | S.X.U.K |
| 041 ## - Language | |
| Language | English |
| 082 ## - DDC NUMBER | |
| Classification number | R 006.31 MUR(MAC) |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Murty, M.N. |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning |
| Sub Title | : theory and practice |
| Statement of responsibility | M N Murty, Ananthanarayana V S. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Kolkata |
| Name of publisher, distributor, etc | University press |
| Date of publication, distribution, etc | c2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | xi, 332p. |
| Other Details | P.B. |
| 500 ## - GENERAL NOTE | |
| General note | Preface<br/>Acknowledgements<br/>List of Acronyms<br/><br/>Chapter 1: Introduction to Machine Learning<br/>Evolution of Machine Learning | Paradigms for ML | Learning by Rote | Learning by Deduction | Learning by Abduction | Learning by Induction | Reinforcement Learning | Types of Data | Matching | Stages in Machine Learning | Data Acquisition | Feature Engineering | Data Representation | Model Selection | Model Learning | Model Evaluation | Model Prediction | Model Explanation | Search and Learning | Explanation Offered by the Model | Data Sets Used<br/><br/>Chapter 2: Nearest Neighbor-Based Models<br/>Introduction to Proximity Measures | Distance Measures | Minkowski Distance |Weighted Distance Measure | Non-Metric Similarity Functions | Levenshtein Distance | Mutual Neighborhood Distance (MND) | Proximity Between Binary Patterns | Different Classification Algorithms Based on the Distance Measures | Nearest Neighbor Classifier (NNC) | K-Nearest Neighbor Classifier | Weighted K-Nearest Neighbor (WKNN) Algorithm | Radius Distance Nearest Neighbor Algorithm | Tree-Based Nearest Neighbor Algorithm | Branch and Bound Method | Leader Clustering | KNN Regression | Concentration Effect and Fractional Norms | Performance Measures | Performance of Classifiers | Performance of Regression Algorithms | Area Under the ROC Curve for the Breast Cancer Data Set<br/><br/>Chapter 3: Models Based on Decision Trees<br/>Introduction to Decision Trees | Decision Trees for Classification | Impurity Measures for Decision Tree Construction | Properties of the Decision Tree Classifier (DTC) | Applications in Breast Cancer Data | Embedded Schemes for Feature Selection | Regression Based on Decision Trees | Bias–Variance Trade-off | Random Forests for Classification and Regression | Comparison of DT and RF Models on Olivetti Face Data | AdaBoost Classifier | Regression Using DT-Based Models | Gradient Boosting (GB) | Practical Application<br/><br/>Chapter 4: The Bayes Classifier<br/>Introduction to the Bayes Classifier | Probability, Conditional Probability and Bayes’ Rule | Conditional Probability | Total Probability | Bayes’ Rule and Inference | Bayes’ Rule and Classification | Random Variables, Probability Mass Function, Probability Density Function and Cumulative Distribution Function, Expectation and Variance | Random Variables | Probability Mass Function (PMF) | Binomial Random Variable | Cumulative Distribution Function (CDF) | Continuous Random Variables | Expectation of a Random Variable | Variance of a Random Variable | Normal Distribution | The Bayes Classifier and its Optimality | Multi-Class Classification | Parametric and Non-Parametric Schemes for Density Estimation | Parametric Schemes | Class Conditional Independence and Na.ve Bayes Classifier | Estimation of the Probability Structure | Naive Bayes Classifier (NBC)<br/><br/>Chapter 5: Machine Learning Based on Frequent Itemsets<br/>Introduction to the Frequent Itemset Approach | Frequent Itemsets | Frequent Itemset Generation | Frequent Itemset Generation Strategies | Apriori Algorithm | Frequent Pattern Tree and Variants | FP Tree-Based Frequent Itemset Generation | Pattern Count (PC) Tree-Based Frequent Itemset Generation | Frequent Itemset Generation Using the PC Tree | Dynamic Mining of Frequent Itemsets | Classification Rule Mining | Frequent Itemsets for Classification Using PC Tree | Frequent Itemsets for Clustering Using the PC Tree<br/><br/>Chapter 6: Representation<br/>Introduction to Representation | Feature Selection | Linear Feature Extraction | Vector Spaces | Basis of a Vector Space | Row Vectors and Column Vectors | Linear Transformations | Eigenvalues and Eigenvectors | Symmetric Matrices | Rank of a Matrix | Principal Component Analysis | Experimental Results on Olivetti Face Data | Singular Value Decomposition | PCA and SVD | Random Projections<br/><br/>Chapter 7: Clustering<br/>Introduction to Clustering | Partitioning of Data | Data Re-organization | Data Compression | Summarization | Matrix Factorization | Clustering of Patterns | Data Abstraction | Clustering Algorithms | Divisive Clustering | Agglomerative Clustering | Partitional Clustering | K-Means Clustering | K-Means++ Clustering | Soft Partitioning | Soft Clustering | Fuzzy C-Means Clustering | Rough Clustering | Rough K-Means Clustering Algorithm | Expectation Maximization-Based Clustering | Spectral Clustering | Clustering Large Data Sets | Divide-and-Conquer Method<br/><br/>Chapter 8: Linear Discriminants for Machine Learning<br/>Introduction to Linear Discriminants | Linear Discriminants for Classification | Parameters Involved in the Linear Discriminant Function | Learning w and b | Perceptron Classifier | Perceptron Learning Algorithm | Convergence of the Learning Algorithm | Linearly Non-Separable Classes | Multi-Class Problems | Support Vector Machines | Linearly Non-Separable Case | Non-linear SVM | Kernel Trick | Logistic Regression | Linear Regression | Sigmoid Function | Learning w and b in Logistic Regression | Multi-Layer Perceptrons (MLPs) | Backpropagation for Training an MLP | Results on the Digits Data Set<br/><br/>Chapter 9: Deep Learning<br/>Introduction to Deep Learning | Non-Linear Feature Extraction Using Autoencoders | Comparison on the Digits Data Set | Deep Neural Networks | Activation Functions | Initializing Weights | Improved Optimization Methods | Adaptive Optimization | Loss Functions | Regularization | Adding Noise to the Output or Label Smoothing | Experimental Results on the MNIST Data Set | Convolutional Neural Networks | Convolution | Padding Zero Rows and Columns | Pooling to Reduce Dimensionality | Recurrent Neural Networks | Training an RNN | Encoder–Decoder Models | Generative Adversarial Networks<br/><br/>Conclusions<br/>Appendix – Hints to Practical Exercises<br/>Index |
| 650 ## - Subject | |
| Subject | MACHINE LEARNING |
| 700 ## - Added Entry Personal Name | |
| Relator Code | auth. |
| Added Entry Personal Name | V.S. Ananthanarayana |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | REFERENCE COMPUTER SCIENCE |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Koha collection | Location (home branch) | Sublocation or collection (holding branch) | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Serial Enumeration / chronology | Koha issues (times borrowed) | Koha full call number | Barcode (Accession No.) | Koha date last seen | Copy Number | Price effective from | Koha item type |
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| Dewey Decimal Classification | Not For Loan | Reference | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Reference Section | 02/18/2026 | K.M. Enterprise | 850.00 | S.X.U.K | R 006.31 MUR(MAC) | UCS13928 | 02/18/2026 | 13928 | 02/18/2026 | REFERENCE COMPUTER SCIENCE | ||||
| Dewey Decimal Classification | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Lending Section | 02/18/2026 | K.M. Enterprise | 850.00 | S.X.U.K | 006.31 MUR(MAC) | CS13929 | 02/18/2026 | 13929 | 02/18/2026 | COMPUTER SCIENCE | ||||||
| Dewey Decimal Classification | St. Xavier's University, Kolkata | St. Xavier's University, Kolkata | Lending Section | 02/18/2026 | K.M. Enterprise | 850.00 | S.X.U.K | 006.31 MUR(MAC)C1 | CS13930 | 02/18/2026 | 13930 | 02/18/2026 | COMPUTER SCIENCE |