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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

How to Do Things With Words

Obtaining a good statistical representation of material microstructures is crucial for establishing robust process–structure–property linkages and machine learning techniques can bridge this gap. One major difficulty in leveraging recent advances in deep learning for this purpose is the scarcity of good quality data with enough metadata. In machine learning, similarity metric learning using Siamese networks has been used to deal with sparse data. Inspired by this, the authors propose a Siamese architecture to learn microstructure representations. The authors show that analysis tasks such as the classification of microstructures can be done more efficiently in the learned representation space.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8D181BE0062A8DE7EEA333F726ECBC66/S2159685920000555a.pdf/div-class-title-microstructure-representation-learning-using-siamese-networks-div.pdf

Microstructure representation learning using Siamese networks

Parallel corpora are often injected with bilingual lexical resources for improved Indian language machine translation (MT). In absence of such lexical resources, multilingual topic models have been used to create coarse lexical resources in the past, using a Cartesian product approach. Our results show that for morphologically rich languages like Hindi, the Cartesian product approach is detrimental for MT. We then present a novel ‘sentential’ approach to use this coarse lexical resource from a multilingual topic model. Our coarse lexical resource when injected with a parallel corpus outperforms a system trained using parallel corpus and a good quality lexical resource. As demonstrated by the quality of our coarse lexical resource and its benefit to MT, we believe that our sentential approach to create such a resource will help MT for resource-constrained languages.

That'll do fine! A coarse lexical resource for English-Hindi MT, using polylingual topic models

In this paper we take an important step towards completely unsupervised stemming by giving a scheme for semi supervised stemming. The input to the system is a list of word forms and suffixes. The motivation of the work comes from the need to create a root or stem identifier for a language that has electronic corpora and some elementary linguistic work in the form of, say, suffix list. The scope of our work is suffix based morphology, (i.e., no prefix or infix morphology). We give two greedy algorithms for stemming. We have performed extensive experimentation with four languages: English, Hindi, Malayalam and Marathi. Accuracy figures ranges from 80% to 88% are reported for all languages.

Little by Little: Semi Supervised Stemming through Stem Set Minimization

Samasa-karta: An online tool for producing compound words using IndoWordNet

features process input and output strings finite-state (string) automata and transducers (weighted) input string → translation (WFST) → output strings → reranking (WFSA) → final output features process input and output strings finite-state (string) automata and transducers (weighted)

Pushpak Bhattacharyya

Papers

Microstructure representation learning using Siamese networks

That'll do fine! A coarse lexical resource for English-Hindi MT, using polylingual topic models

Little by Little: Semi Supervised Stemming through Stem Set Minimization

Samasa-karta: An online tool for producing compound words using IndoWordNet

Phrase-Based Machine Translation