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

By exploiting the interactivity and processing power of mobile technologies, an immersive learning experience can be created. iJDSP and AJDSP are mobile graphical programming applications for simulation and visualization of signal processing concepts, developed to complement instruction to students from the STEM fields. In this paper, the enhanced sensing capabilities of modern mobile devices have been explored by developing DSP tools that allow interfaces to on-board and external sensors. An improvement in conceptual understanding by performing custom designed laboratory exercises using the proposed application was observed in the preliminary assessments.

Development of mobile sensing apps for DSP applications

In regression analysis, outliers in the data can induce a bias in the learned function, resulting in larger errors. In this paper we derive an empirically estimable bound on the regression error based on a Euclidean minimum spanning tree generated from the data. Using this bound as motivation, we propose an iterative approach to remove data with noisy responses from the training set. We evaluate the performance of the algorithm on experiments with real-world pathological speech (speech from individuals with neurogenic disorders). Comparative results show that removing noisy examples during training using the proposed approach yields better predictive performance on out-of- sample data.

Removing data with noisy responses in regression analysis

Sonic representations of spaces have emerged as a means to capture and present the activity that conventional representations, such as maps, do not encapsulate. Therefore, to convey the activity information of regions, both large and small, we use sounds and information provided by regional communities in the automated design of soundscapes to re-sonify geographic sound activity. To quantify this community knowledge, we have developed an ontological framework to determine the importance of sound and concepts to one another using acoustic, semantic, and social information. This framework is then used in the automated design of a generative soundscape model purposed to identify and re-sonify sounds that impart relevant information about a geographic region. Furthermore, we are developing a social networking website to facilitate the collection and re-sonification of sounds and data.

/pdf/re-sonification-of-geographic-sound-activity-using-acoustic-45pl30do5q.pdf

Re-sonification of geographic sound activity using acoustic, semantic, and social information

In this paper, we describe Web-based educational software tools tailored to expose students in an undergraduate DSP course to the basics of hidden Markov model (HMM)-based speech recognition. In particular, we developed Java software that enables on-line computer laboratories on the essential preprocessing, HMM, and Viterbi algorithms as used in a basic speech recognition task. The software is complemented by streaming lectures, a set of on-line demonstrations with animation, and exercises that take the student through HMM training and recognition. The software is being made available to students in the Fall of 2003 and we expect to present assessment results at the conference.

Web-based experiments for introducing speech recognition basics in a DSP course

Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multi-layered case. In this paper, we consider the problem of semi-supervised learning with multi-layered graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning, and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer dependencies for building multi-layered graph embeddings. Using empirical studies on several benchmark datasets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison to state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

Andreas Spanias

Papers

Development of mobile sensing apps for DSP applications

Removing data with noisy responses in regression analysis

Re-sonification of geographic sound activity using acoustic, semantic, and social information

Web-based experiments for introducing speech recognition basics in a DSP course

Attention Models with Random Features for Multi-layered Graph Embeddings.