Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The current state of the art in judging pathological speech intelligibility is subjective assessment performed by trained speech pathologists (SLP). These tests, however, are inconsistent, costly and, oftentimes suffer from poor intra- and inter-judge reliability. As such, consistent, reliable, and perceptually-relevant objective evaluations of pathological speech are critical. Here, we propose a data-driven approach to this problem. We propose new cost functions for examining data from a series of experiments, whereby we ask certified SLPs to rate pathological speech along the perceptual dimensions that contribute to decreased intelligibility. We consider qualitative feedback from SLPs in the form of comparisons similar to statements “Is Speaker A's rhythm more similar to Speaker B or Speaker C?” Data of this form is common in behavioral research, but is different from the traditional data structures expected in supervised (data matrix + class labels) or unsupervised (data matrix) machine learning. The proposed method identifies relevant acoustic features that correlate with the ordinal data collected during the experiment. Using these features, we show that we are able to develop objective measures of the speech signal degradation that correlate well with SLP responses.

Modeling pathological speech perception from data with similarity labels

Bayesian optimization (BO) has been widely applied to several modern science and engineering applications such as machine learning, neural networks, robotics, aerospace engineering, experimental design. BO has emerged as the modus operandi for global optimization of an arbitrary expensive to evaluate black box function f. Although BO has been very successful in low dimensions, scaling it to high dimensional spaces has been significantly challenging due to its exponentially increasing statistical and computational complexity with increasing dimensions. In this era of high dimensional data where the input features are of million dimensions scaling BO to higher dimensions is one of the important goals in the field. There has been a lot of work in recent years to scale BO to higher dimensions, in many of these methods some underlying structure on the objective function is exploited. In this paper, we review recent efforts in this area. In particular, we focus on the methods that exploit different underlying structures on the objective function to scale BO to high dimensions.

Bayesian Optimization in High-Dimensional Spaces: A Brief Survey

We consider a distributed estimation problem over fading multiple-access channels. Several sensors transmit a parameter to a fusion center using an amplify-and-forward scheme. The fusion center has multiple antennas and uses the transmissions from the sensors to estimate the parameter. In this paper we will assume that the sensors have full channel state information and formulate an optimization problem that needs to be solved to select the best sensor gains. We also propose bounds on the performance and two practical methods that can be implemented in this scenario. By comparing the performance, it is shown that though there is benefit in having multiple antennas at the fusion center, when full channel information is available at the sensors, the gain in performance is at most a factor of 2.

Distributed estimation over fading macs with multiple antennas at the fusion center

Pair-wise error probability (PEP) is analyzed in the presence of channel estimation error (CEE) for group linear constellation precoded orthogonal frequency division multiplexing (GLCP-OFDM). It is observed that the CEE does not reduce the diversity order but contributes to a loss of coding gain. Furthermore, the proposed optimal power allocation scheme based on the derived PEP has performance that converges to the performance with perfect channel state information when the number of subcarriers to the channel length ratio increases.

Performance of precoded OFDM with channel estimation error

This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated fault detection methods are also described to predict and by pass failing modules. Finally, the fully connected solar array, which is fitted with multiple sensors, is operated as an Internet of things network. Integrated with each module are sensors and radio electronics communicating all data to a fusion center. Gathering data at the fusion center to compute and transmit analytics requires secure low power communication solutions. To optimize the resources and power consumption, we describe a method to integrate fully distributed algorithms designed for a wireless sensor network in this CPS system.

Andreas Spanias

Papers

Modeling pathological speech perception from data with similarity labels

Bayesian Optimization in High-Dimensional Spaces: A Brief Survey

Distributed estimation over fading macs with multiple antennas at the fusion center

Performance of precoded OFDM with channel estimation error

Shading prediction, fault detection, and consensus estimation for solar array control