Signal Processing and Machine Learning Theory
Series: Academic Press Library in Signal Processing;
- Publisher's listprice EUR 144.00
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61 084 Ft (58 176 Ft + 5% VAT)
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- Discount 20% (cc. 12 217 Ft off)
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61 084 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Academic Press
- Date of Publication 29 November 2023
- ISBN 9780323917728
- Binding Paperback
- No. of pages1234 pages
- Size 234x190 mm
- Weight 1840 g
- Language English 558
Categories
Long description:
Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc.
MoreTable of Contents:
1. Introduction to Signal Processing and Machine Learning Theory
2. Continuous-Time Signals and Systems
3. Discrete-Time Signals and Systems
4. Random Signals and Stochastic Processes
5. Sampling and Quantization
6. Digital Filter Structures and Their Implementation
7. Multi-rate Signal Processing for Software Radio Architectures
8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
9. Discrete Multi-Scale Transforms in Signal Processing
10. Frames in Signal Processing
11. Parametric Estimation
12. Adaptive Filters
13. Signal Processing over Graphs
14. Tensors for Signal Processing and Machine Learning
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation
Signal Processing and Machine Learning Theory
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