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

Internet of Things (IoT) has enabled several applications related to data analytics. In this paper, an intuitive method for optimizing activity detection data is presented. Further applications include exploring detection accuracies of physical activities such as walking intensity and movement on stairs. This method utilizes different Microcontroller Units (MCUs) with embedded sensors which are used for activity detection. Additionally, this method also incorporates supervised learning - more specifically the Fine Gaussian SVM, to generate a predictive model for activity optimization.

Optimizing Activity Detection via Sensor Fusion

A number of improved algorithms for phase prediction and frame interpolation in the context of sinusoidal speech coding are presented. A minimum-variance sinusoidal phase estimation scheme is proposed. It is shown that reasonably accurate estimates for short-time sinusoidal phases corresponding to voiced frames can be obtained. In addition, improved algorithms for interpolation of sine wave parameters are presented which result in further reduction in bit rate while preserving the subjective equality of the reproduced speech at low bit rates. The performance of the proposed algorithms were evaluated on a large speech database and the results of statistical analysis are provided. The proposed algorithms were successfully integrated into a 2.4 kbps sinusoidal coder, where speech of good quality intelligibility, and naturalness was obtained.

Minimum-variance phase prediction and frame interpolation algorithms for low bit rate sinusoidal speech coding

Improved energy usage data from smart meters offers a unique opportunity to apply advanced analytics that can dramatically improve load forecasting. Utility companies, policy makers, and consumers benefit with better integration of renewables and overall energy management in the IoT digital age. Accurate short-term energy forecasting is essential to improving energy efficiency, reducing blackouts, and enabling smart grid control. In this work-in-progress (WIP) paper, we use individual residential load data to perform customer segmentation based on energy profiles, introduce a unique data segmentation and feature extraction technique based on inherent load signal periodicities, and use deep learning to perform fast and accurate short-term forecasting. Partnering with Prime Solutions Group, a veteran-owned company based in Arizona, we found that we could obtain up to a 12% improvement in hourly one-day forecasting using our custom data segmentation and feature extraction techniques with neural network methods.

Machine Learning For Fast Short-Term Energy Load Forecasting

This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features extracted from this signal, the app measures distances and determines how the space can be utilized for various application such as libraries, conference rooms, or laboratories. The application and related algorithms use concepts such cross-correlation, feature extraction, learning/training algorithms, and discrimination/decision making. These concepts are typically covered in undergraduate classes such as Digital Signal Processing, Control Systems, and Probability and Statistics; and graduate-level classes such as Pattern Recognition and Detection and Estimation Theory. The app is used to create compelling demonstrations and immersive exercises to teach basic concepts related to signal processing and machine learning. Undergraduate student hands-on workshops and outreach activities are planned to evaluate the effectiveness of this approach. Assessment results will be presented at the conference.

Signal processing and machine learning concepts using the reflections echolocation app

Although adaptive gradient algorithms are simple and relatively robust, they generally have poor performance in the absence of "rich" excitation. In particular, it is well known that the convergence speed of the LMS algorithm deteriorates when the condition number of the input autocorrelation matrix is large. This problem has been previously addressed using weighted RLS or normalized frequency-domain algorithms. We present a new approach that employs gradient projections in selected eigenvector sub-spaces to improve the convergence properties of LMS algorithms for colored inputs. We also introduce an efficient method to iteratively update an "eigen subspace" of the autocorrelation matrix. The proposed algorithm is more efficient in terms of computational complexity, than the WRLS and its convergence speed approaches that of the WRLS even for highly correlated inputs. >

Andreas Spanias

Papers

Optimizing Activity Detection via Sensor Fusion

Minimum-variance phase prediction and frame interpolation algorithms for low bit rate sinusoidal speech coding

Machine Learning For Fast Short-Term Energy Load Forecasting

Signal processing and machine learning concepts using the reflections echolocation app

Fast adaptive algorithms using eigenspace projections