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

A recently proposed measure, namely Transfer Entropy (TE), is used to estimate the direction of information flow between coupled linear and nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction of information flow and quantifying the level of interaction between observed data series from coupled systems. We demonstrate the potential usefulness of the improved method through simulation examples with coupled nonlinear chaotic systems. The statistical significance of the results is shown through the use of surrogate data. The improved TE method is then used for the study of information flow in the epileptic human brain. We illustrate the application of TE to electroencephalographic (EEG) signals for the study of localization of the epileptogenic focus and the dynamics of its interaction with other brain sites in two patients with Temporal Lobe Epilepsy (TLE).

Information Flow in Coupled Nonlinear Systems: Application to the Epileptic Human Brain

A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.

Non-Linear Distributed Average Consensus Using Bounded Transmissions

The Java-DSP (J-DSP) on-line laboratory software has been developed from the ground up at Arizona State University to support the computer lab portion of the senior-level DSP course. J-DSP provides capabilities for web-based DSP simulations that can be run using a PC with a Java-enabled browser. J-DSP is accompanied by exercises that actively engage students in several concepts including Z-transforms, filter design, spectral analysis, and random signal processing. Tools and on-line instruments for assessment of the J-DSP software and the associated lab exercises have been developed and described in this letter. Statistical analysis of the pre/post assessment data revealed that the effect of student involvement and the student learning has been enhanced by using J-DSP.

On-line signal processing using J-DSP

Time-frequency representations (TFRs) such as the spectrogram are important two-dimensional tools for processing time-varying signals. In this paper, we present the Java software module we developed for the spectrogram implementation together with the associated programming environment. Our aim is to introduce to students the advanced concepts of TFRs at an early stage in their education without requiring a rigorous theoretical background. We developed two sets of exercises using the spectrogram based on signal analysis and speech processing together with on-line evaluation forms to assess student learning experiences. In the paper, we also provide the positive statistical and qualitative feedback we obtained when the Java software and corresponding exercises were used in a signal processing course.

Advanced concepts in time-frequency signal processing made simple

This paper presents various on-line computer laboratory exercises and related assessment results for Java-Digital Signal-Processing-Controls (J-DSP-C). J-DSP- C is an object oriented simulation environment that is intended for control systems simulations. All J-DSP-C component functions appear as graphical blocks that can be connected to form a variety of educational controls simulations. Dynamical systems with various interconnection topologies can be developed and simulated while results can be examined graphically. J-DSP-C has been designed to be easily accessible through the internet with any computer system equipped with a Java -enabled browser.

http://jdsp.engineering.asu.edu/needs/papers/FIE-2003-CONTROLS.pdf

Andreas Spanias

Papers

Information Flow in Coupled Nonlinear Systems: Application to the Epileptic Human Brain

Non-Linear Distributed Average Consensus Using Bounded Transmissions

On-line signal processing using J-DSP

Advanced concepts in time-frequency signal processing made simple

J-DSP-C, a control systems simulation environement: labs and assessment