COMMENTS

  1. Undergraduate Fundamentals of Machine Learning

    of the basics of machine learning, it might be better understood as a collection of tools that can be applied to a speci c subset of problems. 1.2 What Will This Book Teach Me? The purpose of this book is to provide you the reader with the following: a framework with which to approach problems that machine learning learning might help solve ...

  2. Master Thesis Using Machine Learning Methods for Evaluating ...

    Based on this background, the aim of this thesis is to select and implement a machine learning process that produces an algorithm, which is able to detect whether documents have been translated by humans or computerized systems. This algorithm builds the basic structure for an approach to evaluate these documents. 1.2 Related Work

  3. ADVERSARIALLY ROBUST MACHINE LEARNING WITH GUARANTEES A ...

    Machine learning (ML) systems are remarkably successful on a variety of benchmarks across sev- eral domains. In these benchmarks, the test data points, though not identical, are very similar to

  4. Undergraduate Fundamentals of Machine Learning

    Undergraduate Fundamentals of Machine Learning. The initial version of this textbook was created by William J. Deuschle for his senior thesis, based on his notes of CS181 during the Spring of 2017. This textbook has since been maintained by the CS181 course staff with bug fixes from many CS181 students. Contents.

  5. Artificial Intelligence and Machine Learning: Current ...

    intelligence and machine learning. This thesis will define machine learning and artificial intelligence for the investor and real estate audience, examine the ways in which these new analytic, predictive, and automating technologies are being used in the real estate industry, and postulate potential

  6. New Theoretical Frameworks for Machine Learning

    methods has been severely lacking. In this thesis, we develop theoretical foundations and new algorithms for several important emerging learning paradigms of significant practical importance, including Semi-Supervised Learning, Active Learning, and Learning with Kernels and more general similarity functions.

  7. Machine learning for detection of cyberattacks on industrial ...

    This thesis aims to help researchers and industry leaders understand how to implement machine learning (ML) as an early detection tool for anomalies (cyberattacks being a subset of anomalies) in their processes. With learnings from an end-to-end implementation of some stateart machine learning models and a -of-the-

  8. Integrating Machine Learning into Data Analysis and Plant ...

    Acomprehensivedatalake would enable machine learning and other analytics to comb through plant data to determine what variables are most impactful on overall plant performance. Those key metrics can then be included and relied on for benchmarking and performance analysis.

  9. ABSTRACT USING MACHINE LEARNING TECHNIQUES FOR ANALYZING ...

    This thesis uses machine learning techniques and statistical analysis in two separate educational experiments. In the first experiment we attempt to find relationships between students’ written essay responses to physics questions and their learning of the physics data.

  10. Masters Thesis: A Deep Learning Prediction Model for Object ...

    This thesis report is structured into five chapters. Chapter 2 provides a theoretical expla-nation of machine learning theory. Chapter three reviews four of the most popular machine learning theories: decision trees, artificial neural networks, support vector machines and k-Nearest-Neighbor classification.