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

Copyright © 2005, American Society for Engineering Education” Abstract This paper describes the roadmap followed to develop a prototype, obtain support for its full implementation, embed in the curriculum and assess, and then disseminate. We outline specifically how the J-DSP online laboratory concept was conceived, how the prototype “proof of concept” version developed, how a more elaborate version was embedded in the curriculum, and the way it was eventually disseminated. Even though the paper describes our experiences in the signal processing area the idea can be extended to other areas as well. Index Terms – Web-based instruction, on-line laboratories, interactive DSP simulations, assessment instruments, J-DSP dissemination, FIE workshop, and CCLI EMD proposal

The java-DSP (J-DSP) project - From the prototype to the full implementation and dissemination

Signal processing algorithms and DSP chips are embedded nearly in every application that involves natural signal or data analysis and/or synthesis. Applications of digital signal processing (DSP) in engineering include electrical, mechanical, chemical, industrial and biomedical systems. Applications in other areas include entertainment, financial, health, computing, manufacturing, to name a few. At ASU we developed an elective course for an undergraduate program called Digital Culture that includes gaming, smart stages, computer music, visualization and other applications. We have offered the course online to arts majors in 2013. We begun adding multidisciplinary application content to this course and offered it again in 2015 as a hybrid online course with compulsory weekly on-campus sessions. Arrangements are being made to include it as an elective course in information management systems, computer informatics, mechanical engineering, and biomedical informatics. The course now includes several introductory topics in signal processing covered mostly at a qualitative and block diagram level; we added several simulations in MATLAB and in Java-DSP. The course covers basics of DSP starting from time and frequency domain analysis and sampling. It then covers digital FIR ad IIR filters and the FFT. About one third of the course covers applications which introduce qualitative descriptions of some advanced topics. For example, linear prediction and coding of speech are described at the block diagram level with MATLAB and Java simulations. Extensions to 2-D signal processing are covered as well with the focus on JPEG and MPEG applications. The syllabus, simulations and preliminary assessments of this course are presented in the paper.

An introductory signal processing course offered across the curriculum

Sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. Most perceptual sinusoidal component selection strategies are computationally intensive and not suitable for real-time applications. In this paper, a computationally efficient sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. The hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent sinusoidal components. Then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. Experimental results show that the proposed technique maintains a low perceptual sinusoidal synthesis error at a much lower computational complexity.

Low-complexity sinusoidal component selection using loudness patterns

An integrated sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system includes a low noise floor analog front-end followed by a signal processing stage. Classification methods are used since the pollutants are often encountered as complex mixtures. In this paper, we present techniques to detect, digitize and classify analytes. This is done by extracting appropriate features from sensor data and using pattern recognition methods to identify the analytes. The detection analog front-end achieves 54dB SNR. The low-noise digitization technique is presented along with the feature extraction and classification algorithms. Comparative results are given for a series of pattern classifiers.

Andreas Spanias

Papers

The java-DSP (J-DSP) project - From the prototype to the full implementation and dissemination

An introductory signal processing course offered across the curriculum

Low-complexity sinusoidal component selection using loudness patterns

A real-time monitoring system for diesel and gasoline exhaust exposure

Performance enhancement of the frequency-domain lms adaptive algorithm.