An introduction to machine learning / Miroslav Kubat.
Publication details: Cham : Springer, 2017Description: xiii, 348 pages : illustrations, charts ; 25 cmISBN:- 9783319639123
- Q325.5 .K83 2017
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | URBE Library General Stacks | Non-fiction | Q325.5 .K83 2017 (Browse shelf(Opens below)) | Available | 0884 |
Browsing URBE Library shelves, Shelving location: General Stacks, Collection: Non-fiction Close shelf browser (Hides shelf browser)
Q180.55.M4 B66 2016 Copy 2 The craft of research / | Q180.55.M4 L43 2016 Copy 1 Practical research : | Q180.55.M4 L43 2016 Copy 2 Practical research : | Q325.5 .K83 2017 An introduction to machine learning / | QA76.27 .G37 2019 C.2 Go! all in one : computer concepts and applications / | QA76.27 .G37 2020 C.1 Go! all in one : computer concepts and applications / | QA76.3 D598 2012 Comptia a+ complete deluxe study guide 2e (exams 220-801 and 220-802) / |
1 A Simple Machine-Learning Task --
2 Probabilities: Bayesian Classifiers --
Similarities: Nearest-Neighbor Classifiers --
4 Inter-Class Boundaries: Linear and Polynomial Classifiers --
5 Artificial Neural Networks --
6 Decision Trees --
7 Computational Learning Theory --
8 A Few Instructive Applications --
9 Induction of Voting Assemblies --
10 Some Practical Aspects to Know About --
11 Performance Evaluation --
12 Statistical Significance --
13 Induction in Multi-Label Domains --
14 Unsupervised Learning --
15 Classifiers in the Form of Rulesets --
16 The Genetic Algorithm --
17 Reinforcement Learning.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work
There are no comments on this title.