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

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. The authors used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. They conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures. They showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, they developed three GLCM implementations and evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured. These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1053&context=csearticles

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

In this paper, a multiagent-based model is used to study distributed energy management in a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous agents, capable of making local decisions in order to maximize their own profit in a multiagent environment. For every supplier, a lack of information about customers and other suppliers creates challenges to optimal decision making in order to maximize its return. Similarly, customers face difficulty in scheduling their energy consumption without any information about suppliers and electricity prices. Additionally, there are several uncertainties involved in the nature of MGs due to variability in renewable generation output power and continuous fluctuation of customers’ consumption. In order to prevail over these challenges, a reinforcement learning algorithm was developed to allow generation resources, distributed storages, and customers to develop optimal strategies for energy management and load scheduling without prior information about each other and the MG system. Case studies are provided to show how the overall performance of all entities converges as an emergent behavior to the Nash equilibrium, benefiting all agents.

Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid

Background

Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self-regulation constructs.



Purpose (Hypothesis)

This project sought to determine what motivational and self-regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self-regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed.



Design/Method

Five hundred and thirty-eight students, 332 of them engineering majors, were surveyed on motivation and self-regulation variables. Data were analyzed from a learner-centered profile approach using cluster analysis.



Results

We obtained a five-cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering-tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not.



Conclusions

Findings indicate the motivational and self-regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self-regulated learning scaffolds in the classroom.

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

A streamflow drought event, defined by applying the threshold level approach on streamflow time series, is composed of three parameters: duration, severity and magnitude. This study reveals statistical characteristics on streamflow drought event parameters and detects spatial and temporal trends in the streamflow drought in terms of frequency, duration and severity in Nebraska. The studies are conducted on three time periods: 1970–2001 (60 stations), 1950–2001 (43 stations), and 1932–2001 (9 stations). The statistical tests performed on the drought event parameters include correlation between event parameters tests, Hurst coefficients and lag-one coefficients, and trend-free pre-whitening Mann–Kendall (TFPW-MK) tests. The analysis shows that there is no uniform trend on the streamflow drought in the whole state. However, some trends are evident for specific regions. Specifically, it is most likely that droughts in the Republican watershed have become more intense; whereas the drought has become slightly alleviated in the Missouri and nearby watersheds.

/pdf/trend-analysis-of-streamflow-drought-events-in-nebraska-20vvqlmj9x.pdf

Trend Analysis of Streamflow Drought Events in Nebraska

We present an intelligent system for satellite sea ice image analysis named Advanced Reasoning using Knowledge for Typing Of Sea ice (ARKTOS). ARKTOS performs fully automated analysis of synthetic aperture radar (SAR) sea ice images by mimicking the reasoning process of sea ice experts. ARKTOS automatically segments a SAR image of sea ice, generates descriptors for the segments of the image, and then uses expert system rules to classify these sea ice features. ARKTOS also utilizes multisource data fusion to improve classification and performs belief handling using Dempster-Shafer. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and graphical user interface-based knowledge engineering and modification. As a research project over the past ten years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation, deployment, and operationalization at the U.S. National Ice Center. In this paper, we focus on the methodology, evaluations, and classification results of ARKTOS.

/pdf/arktos-an-intelligent-system-for-sar-sea-ice-image-4oi8vacysl.pdf

Leen-Kiat Soh

Papers

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Trend Analysis of Streamflow Drought Events in Nebraska

ARKTOS: an intelligent system for SAR sea ice image classification