"Machine Learning: Algorithms, Models and Applications" ed. by Jaydip Sen

The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation.

Contents
1. Machine Learning in Finance-Emerging Trends and Challenges
2. Design and Analysis of Robust Deep Learning Models for Stock Price Prediction
3. Articulated Human Pose Estimation Using Greedy Approach
4. Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images
5. Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy
6. Enhancing Program Management with Predictive Analytics Algorithms (PAAs)


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