Proposal Template (1)
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California Baptist University *
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123
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English
Date
Apr 3, 2024
Type
docx
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3
Uploaded by SuperHumanEel4009
Gomez 1
Amy Gomez
Professor Grair English 123-AA
18 February 2024
Preliminary Research Proposal
Topic
Machine learning is a fascinating field in my opinion. My interest in machine learning comes from the way we solve problems and interact with technology. Machine learning presents a complex problem that will require a deep understanding of algorithms, statistics, and computer science principles. Machine learning is at the forefront of technological innovation. The ability to
teach machines to learn from data opens up endless possibilities for creating intelligent systems that can automate tasks, make predictions, and even mimic a human.
Research Question and Main Claim How can deep reinforcement learning optimize resource allocation in dynamic cloud computing environments? This research question addresses a specific application area, proposes a cutting-edge technique, and addresses a real-world problem. It opens the door for exploring new algorithms, methodologies, and optimizations within the context of machine learning, while also offering potential practical implications for improving the efficiency and scalability of cloud
computing systems.
Thesis: Machine learning algorithms, when integrated into healthcare systems, offer significant benefits in diagnosis accuracy, treatment optimization, and patient outcomes but put at risk the personal information of their patients if not regulated correctly. Evidence and Sources
Gomez 2
Machine Learning
by Jason Bell talks about various aspects of machine learning, including concepts, algorithms, and applications. It likely provides insights into different machine learning techniques, such as supervised, unsupervised, and reinforcement learning, along with practical examples and case studies.
"Machine Learning: The New AI" by Ethem Alpaydin (Chapters 1 – 4) is a comprehensive book that introduces machine learning concepts and techniques. Ethem Alpaydin is a prominent figure in machine learning, and his book is widely regarded as an excellent resource for beginners and practitioners alike.
I have to analyze still and identify any perspectives, methodologies, or case studies presented in each book that contribute to their value as educational resources. Understanding the main topics and concepts covered in each book, including the approach, depth, and breadth of coverage. I examine the background, credentials, and expertise of the author(s). For academic works, I look for authors who are respected in their field, have relevant academic qualifications, and possess extensive experience in the subject matter. I can also see the sources cited and referenced in the book. High-quality sources typically cite other reputable works, which can provide additional avenues for exploring the topic.
There might not be many major opposing claims for this topic, but different perspectives could exist. Some could see Traditional Methods vs. Cutting – Edge Techniques, even just the essential depth and breadth of the algorithms and the detail it takes to work with machine learning.
Gomez 3
Works Cited
Alpaydin, Ethem . Introduction to Machine Learning
. Cambridge, Massachusetts, The Mit Press,
2014.
Bell, Jason. Machine Learning : Hands-on for Developers and Technical Professionals
. Indianapolis, John Wiley & Sons, 2015.
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