Doctoral Thesis: Novel applications of Machine Learning to NTAP - 7 which is characterized by including neural networks with multiple layers and a variety of architectures and connectivity between Estimated Reading Time: 11 mins Research and Thesis Topics in Machine Learning. Here is the list of current research and thesis topics in Machine Learning: Machine Learning Algorithms. Computer Vision. Supervised Machine Learning. Unsupervised Machine Learning. Deep Learning. Neural Networks. Reinforcement Learning. Predictive Learning. Bayesian Network. Data Mining. Machine Learning AlgorithmsEstimated Reading Time: 12 mins The main topic of the thesis is prediction markets as a machine learning tool. The thesis gives an overview of current state of the art in this research area, relates arti cial prediction markets to existing well-known model combination techniques and shows how they extend blogger.com Size: 1MB
PhD Dissertations - Machine Learning - CMU - Carnegie Mellon University
Data Science Academic Research Featured Post Academia Machine Learning Research posted by Daniel Gutierrez, ODSC June 18, Daniel Gutierrez, ODSC. As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv. org for late-breaking papers that show trends and fertile areas of research.
Other sources of valuable research developments are in the form of Ph. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. In this article, phd thesis machine learning, I present 10 compelling machine learning dissertations that I found interesting in terms of my own areas of pursuit.
Each thesis may take a while to consume but will result in hours of satisfying summer reading. Over the past several years, the use of wearable devices has increased dramatically, primarily for fitness monitoring, largely due to their greater sensor reliability, increased functionality, smaller size, increased ease of use, and greater affordability.
These devices have helped many people of all ages live healthier lives and achieve their personal fitness goals, as they are able to see quantifiable and graphical results of their efforts every step of the way i.
in real-time. Yet, while these device systems work well within the fitness domain, they have yet to achieve a convincing level of functionality in the larger domain of healthcare. The goal of the research detailed in this dissertation is to explore and develop accurate and quantifiable sensing and machine learning phd thesis machine learning for eventual real-time health monitoring by wearable device systems.
To that end, a two-tier recognition system is presented that is designed to identify health activities phd thesis machine learning a naturalistic setting based on accelerometer data of common activities. In Tier I a traditional activity recognition approach is employed to classify short windows of data, while in Tier II these classified windows are grouped to identify instances of a specific activity.
This dissertation proposes efficient algorithms and provides theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, the focus is on two types of non-convex optimization problems: learning the parameters of latent variable models and learning in deep neural networks.
Learning latent variable models is traditionally framed as a non-convex optimization problem through Maximum Likelihood Estimation MLE, phd thesis machine learning. In this research, a novel algorithm is proposed that can flexibly learn a multi-view model in a non-parametric fashion. To scale the nonparametric spectral methods to large datasets, an algorithm called doubly stochastic gradient descent is proposed which uses sampling to approximate two expectations in the problem, and it achieves better balance of computation and statistics by adaptively growing the model as more data arrive.
Learning with neural networks is a difficult non-convex problem while simple gradient-based methods achieve great success in practice, phd thesis machine learning. This part of the research tries to understand the optimization landscape of learning one-hidden-layer networks with Rectified Linear ReLU activation functions.
By directly analyzing the structure of the gradient, it can be shown that neural networks with diverse weights have no spurious local optima. We increasingly depend on algorithms to mediate information and thanks to the advance of computation power and big data, phd thesis machine learning, they do so more autonomously than ever before. At the same time, courts have been deferential to First Amendment defenses made in light of new technology. However, continuing to use the First Amendment as a barrier to regulation may have extreme consequences as our information ecosystem evolves.
There is much interest in embedding data analytics into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and Internet-of-Things to provide these with decision-making capabilities. Such platforms often need to implement machine learning ML algorithms under stringent energy constraints with battery-powered electronics, phd thesis machine learning.
In addition, the memory access latency is a major bottleneck for overall system throughput. To address these issues in memory-intensive inference applications, this dissertation proposes deep in-memory accelerator DIMAwhich deeply embeds computation into the memory array, employing two key principles: 1 accessing and processing multiple rows of memory array at a time, and 2 embedding pitch-matched low-swing analog processing at the periphery of bitcell array.
Large and sparse datasets, such as user ratings over a large collection of items, are phd thesis machine learning in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e. Linear classifiers are popular choices for classifying such data sets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered: a Sparse data and convergence behavior.
How different properties of a data set, such as the sparsity rate and the mechanism of missing data systematically affect convergence behavior phd thesis machine learning classification? b Handling sparse data with non-linear model, phd thesis machine learning. How to efficiently learn non-linear data structures when classifying large sparse data? This dissertation attempts to address these questions with empirical and theoretical analysis on large and sparse data sets.
As the size of Twitter data is increasing, so are undesirable behaviors of its users. One such undesirable behavior is cyberbullying, which could lead to catastrophic consequences.
Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, in real-time if possible. Prevalent approaches to identifying cyberbullying are mainly stand-alone, and thus, are time-consuming. This dissertation proposes a new approach called distributed-collaborative approach for cyberbullying phd thesis machine learning. It contains a network of detection nodes, each of which is independent and capable of classifying tweets it receives.
These detection nodes collaborate with each other in case they need help in classifying a given tweet. The study empirically evaluates various collaborative patterns, phd thesis machine learning, and it assesses the performance of each pattern in detail.
Results indicate an improvement in recall and precision of the detection mechanism over the stand- alone paradigm. Extreme Learning Machine ELM is a training algorithm for Single-Layer Feed-forward Neural Network SLFN. The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, phd thesis machine learning, ELMs achieve performance similar to that of other state-of-the-art training techniques, while taking much less time to train a model.
Experiments show that the speedup of training ELM is up to the 5 orders of magnitude comparing to standard Error Back-propagation algorithm.
ELM is a recently discovered technique that has proved its efficiency in classic regression and classification tasks, phd thesis machine learning, including multi-class cases.
In this dissertation, extensions of ELMs for non-typical for Artificial Neural Networks ANNs problems are presented. The subject of manifold learning is vast and still largely unexplored. As a subset of unsupervised learning it has a fundamental challenge in adequately defining the problem but phd thesis machine learning solution is to an increasingly important desire to understand data sets intrinsically.
It is the overarching goal of this work to present researchers with an understanding of the topic of manifold learning, with a description and proposed method for performing manifold learning, guidance for selecting parameters when applying manifold learning to large scientific data sets and together with open source software powerful enough to meet the demands of big data.
Artificial intelligence and machine learning power many technologies today, from spam filters to self-driving cars to medical decision assistants. While this revolution has hugely benefited from algorithmic developments, it also could not have occurred phd thesis machine learning data, which nowadays is frequently procured at massive scale from crowds. Because data is so crucial, phd thesis machine learning, a key next step towards truly autonomous phd thesis machine learning is the design of better methods for intelligently managing now-ubiquitous crowd-powered data-gathering processes.
This dissertation takes this key next step by developing algorithms for the online and phd thesis machine learning control of these processes. The research considers how to gather data for its two primary purposes: training and evaluation. New computing systems have emerged in response to the increasing size and complexity of modern datasets. For best performance, machine learning methods must be designed to closely align with the underlying properties of these systems.
This dissertation illustrates the impact of system-aware machine learning through the lens of optimization, a crucial component in formulating and solving most machine learning problems. Classically, the performance of an optimization method is measured in terms of accuracy i. and convergence rate after how many iterations? In modern computing regimes, however, it becomes critical to additionally consider a number of systems-related aspects for best overall performance.
These aspects can range from low-level details, such as data structures or machine specifications, to higher-level concepts, such as the tradeoff between communication and computation.
We propose a general optimization framework for machine learning, CoCoA, that gives careful consideration to systems parameters, often incorporating them directly into the method and theory. Daniel D. As a technology journalist, he phd thesis machine learning keeping a pulse on this fast-paced industry.
Daniel is also an educator having taught data science, machine learning and R classes at the university level. Be sure Modeling posted by ODSC Community Nov 22, In the Privacy-Enabling Technologies PETs team at LiveRamp, we design and implement a wide range of AI seaborn Data Visualization posted by ODSC Team Nov 22, Data visualization is one of the most marketable and desirable data science skills out there.
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, time: 20:2810 Compelling Machine Learning Ph.D. Dissertations for
Deep learning is basically more evolved version machine learning and one of the hot topics in machine learning research. In deep learning, machines structure the algorithms in various layers and create artificial neural networks, much similar to the information processing patterns in Human brain, to learn and make intelligent decisions experts, making the process slow, costly, and error prone. The goal of this thesis is to develop methods for automatically extracting the locations of objects such as roads, buildings, and trees directly from aerial images. We investigate the use of machine learning methods trained on aligned aerial Doctoral Thesis: Novel applications of Machine Learning to NTAP - 7 which is characterized by including neural networks with multiple layers and a variety of architectures and connectivity between Estimated Reading Time: 11 mins
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