Search Results - "Machine Learning"

Machine learning : a probabilistic perspective / by Murphy, Kevin P.

Subjects: “…Machine learning. 8118…”
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Machine Learning : For Beginners / by Richards, Ken

Subjects: “…Machine learning. 8118…”
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Machine learning : an algorithmic perspective / by Marsland, Stephen

Subjects: “…Machine learning. 8118…”
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Machine learning : A bayesian and optimization perspective / by Theodoridis, Sergios

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Mathematics for machine learning / by Deisenroth, Marc Peter

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Machine learning and applications /

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Understanding machine learning : from foundations to algorithms / by Shalev-Shwartz, Shai

Subjects: “…Machine learning. 8118…”
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An Introduction to Machine Learning by Rebala, Gopinath, Ravi, Ajay, Churiwala, Sanjay

Table of Contents: “…Introduction -- Basics before Machine Learning -- Learning Models -- Regression -- Improving Further -- Classification -- Clustering (unsupervised Learning) -- Random Forests -- Testing the Algorithm and the Network -- Neural Network -- Reinforcement Learning -- Deep Learning -- Principal Component Analysis -- Anomaly Detection -- Recommender System -- Feature Search/Convolution -- Natural Language Processing -- Language Translation -- AlphaGo -- Data Quality -- System Improvement -- Software stack -- Hardware Implementations. .…”
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Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization / by Sotiropoulos, Dionisios N., Tsihrintzis, George A.

Table of Contents: “…Introduction -- Machine Learning -- The Class Imbalance Problem -- Addressing the Class Imbalance Problem -- Machine Learning Paradigms -- Immune System Fundamentals -- Artificial Immune Systems -- Experimental Evaluation of Artificial Immune System-based Learning Algorithms -- Conclusions and Future Work.…”
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Machine Learning Paradigms Applications of Learning and Analytics in Intelligent Systems /

Table of Contents: “…Chapter 1: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems -- Chapter 2: A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure -- Chapter 3: Differential gene Expression Analysis of RNA-seq Data Using Machine Learning for Cancer Research -- Chapter 4: Machine Learning Approaches for Pap-Smear Diagnosis: An Overview -- Chapter 5: Multi-Kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems -- Chapter 6: Conceptualizing and Measuring Energy Security: Geopolitical Dimensions, Data Availability, Quantitative and Qualitative Methods -- Chapter 7: Automated Stock Price Motion Prediction Using Technical Analysis Datasets and Machine Learning -- Chapter 8: Airport Data Analysis Using Common Statistical Methods and Knowledge-Based Techniques -- Chapter 9: A Taxonomy and Review of the Network Data Envelopment Analysis Literature -- Chapter 10: Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams -- Chapter 11: Analytics and Evolving Landscape of Machine Learning for Emergency Response -- Chapter 12: Social Media Analytics, Types and Methodology -- Chapter 13: Machine Learning Methods for Opinion Mining in Text: The Past and the Future -- Chapter 14: Ship Detection Using Machine Learning and Optical Imagery in the Maritime Environment -- Chapter 15: Video Analytics for Visual Surveillance and Applications: An Overview and Survey -- Chapter 16: Machine Learning in Alternate Testing of Integrated Circuits.…”
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Machine Learning Paradigms Advances in Data Analytics /

Table of Contents: “…Data Analytics in the Medical, Biological and Signal Sciences -- Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics -- Classification Methods in Image Analysis with a Special Focus on Medical Analytics -- Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field -- Machine Learning Methods for the Protein Fold Recognition Problem. .…”
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Machine Learning in Sports Identifying Potential Archers / by Muazu Musa, Rabiu, Taha, Zahari, P.P.Abdul Majeed, Anwar, Abdullah, Mohamad Razali

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Optimization for machine learning /

Subjects: “…Machine learning Mathematical models. 46147…”
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Introduction to machine learning / by Alpaydin, Ethem

Table of Contents: “…Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.…”
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Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics / by Raschka, Sebastian

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Designing Machine Learning Systems. by Huyen, Chip

Table of Contents: “…Cover -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- What This Book Is Not -- Navigating This Book -- GitHub Repository and Community -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Overview of Machine Learning Systems -- When to Use Machine Learning -- Machine Learning Use Cases -- Understanding Machine Learning Systems -- Machine Learning in Research Versus in Production -- Machine Learning Systems Versus Traditional Software -- Summary -- Chapter 2. …”
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Innovations in Biomedical Engineering

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Deep learning with Python / by Chollet, Francois

Subjects: “…Machine learning.…”
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Feature engineering bookcamp / by Ozdemir, Sinan

Subjects: “…Machine learning.…”
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Deep Learning with Python, Second Edition. by Chollet, Francois

Table of Contents: “…-- 1.1 Artificial intelligence, machine learning, and deep learning -- 1.1.1 Artificial intelligence -- 1.1.2 Machine learning -- 1.1.3 Learning rules and representations from data -- 1.1.4 The "deep" in "deep learning" -- 1.1.5 Understanding how deep learning works, in three figures -- 1.1.6 What deep learning has achieved so far -- 1.1.7 Don't believe the short-term hype -- 1.1.8 The promise of AI -- 1.2 Before deep learning: A brief history of machine learning -- 1.2.1 Probabilistic modeling -- 1.2.2 Early neural networks -- 1.2.3 Kernel methods -- 1.2.4 Decision trees, random forests, and gradient boosting machines -- 1.2.5 Back to neural networks -- 1.2.6 What makes deep learning different -- 1.2.7 The modern machine learning landscape -- 1.3 Why deep learning? …”
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