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

This Innovative Practice Work in Progress Paper describes the experience and assessment of introducing machine learning concepts in a sophomore signals and systems course. Advanced machine learning concepts are typically covered in graduate level courses. However, as machine learning applications become more and more ubiquitous in our daily lives, it is important to expose students to machine learning concepts early at the undergraduate level. Signals and Systems I is a sophomore level course in the Electrical Engineering online bachelor degree curriculum. As the first course in signals and systems, it focuses on the basic concepts including signal transformation, linear time-invariant systems, Fourier series, Fourier transforms, Laplace and Z transforms. The course was taught using lecture videos and reading materials. MATLAB labs were also incorporated to introduce students to practical applications. Feedback from students showed their preference for more real world applications. This paper describes how a web-based simulation lab exercise was introduced to expose students to machine learning concepts. Specifically, students made the connection between machine learning and signals and systems concepts through a speech recognition application. In particular, students applied spectral analysis and identified voice features through pole/zero representation. To evaluate the effectiveness of the exercise, statistics from pre/post quizzes as well as student comments from a survey is analyzed.

Introducing Machine Learning in a Sophomore Signals and Systems Course

An analysis/synthesis technique based on harmonic sinusoidal modeling of speech is used to develop a new hidden Markov model (HMM) based speech enhancement algorithm State sequence estimation is done using a standard HMM-based approach State-based enhancement is carried out by assuming a harmonic model for speech, ie, by representing each block of speech as a sum of sine waves in terms of a set of amplitudes, phases, and harmonically related frequencies Given the maximum a-posteriori probability (MAP) state sequence, the amplitudes, phases, voicing, and fundamental frequency are estimated Simulation results are presented, comparing the performance of the proposed algorithm to that of a standard HMM-based approach The proposed method was found to reduce the structured residual noise normally associated with HMM-based algorithms >

Speech enhancement using a state-based transform model

Using J-DSP and labview to perform undergraduate

Analysis / Synthesis of Speech Using the Short-Time Fourier Transform and a Time-Varying ARMA Process

This paper presents Java DSP functions and modules that we have developed to familiarize students with the mixed-signal application of digital signal processing (DSP) via interactive simulation software targeting the highlighting of fundamental design tradeoffs. The new functions implement several advanced methods and demonstrate tradeoffs that relate to the areas of digital communications, digital signal processing, and radio-frequency circuit design. The Java functions developed have been tested and validated within our research group. Tutorials for each of the three modules have been developed and used with mixed upper-level undergraduate and entry-level graduate student classes. These functions have been assessed and they were shown to help students considerably in acquiring familiarity with basic concepts as well as various design tradeoffs using the presented training modules.

Andreas Spanias

Papers

Introducing Machine Learning in a Sophomore Signals and Systems Course

Speech enhancement using a state-based transform model

Using J-DSP and labview to perform undergraduate

Analysis / Synthesis of Speech Using the Short-Time Fourier Transform and a Time-Varying ARMA Process

Java functions for power amplifier linearization techniques