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- Machine Learning and Artificial Intelligence
- Machine Learning A Probabilistic Approach
- Probabilistic machine learning and artificial intelligence

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This includes the design of new AI methods, development of AI algorithms and tools with a view at expanding the reach of AI and its generalization abilities. In particular, we study foundational issues of robustness, safety, trust, reliability, tractability, scalability, interpretability and explainability of AI. The concrete research direction will be determined together with the successful candidate.

## Machine Learning and Artificial Intelligence

Machine learning ML is the study of computer algorithms that improve automatically through experience. Machine learning algorithms build a model based on sample data, known as " training data ", in order to make predictions or decisions without being explicitly programmed to do so.

A subset of machine learning is closely related to computational statistics , which focuses on making predictions using computers; but not all machine learning is statistical learning.

The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.

Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks.

For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms.

In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step. The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available.

In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm s it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used. Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system:.

Other approaches have been developed which don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling , dimensionality reduction or meta learning. As of , deep learning has become the dominant approach for much ongoing work in the field of machine learning. The term machine learning was coined in by Arthur Samuel , an American IBMer and pioneer in the field of computer gaming and artificial intelligence.

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T , as measured by P , improves with experience E. This follows Alan Turing 's proposal in his paper " Computing Machinery and Intelligence ", in which the question "Can machines think?

Modern day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models.

A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. Where as, a machine learning algorithm for stock trading may inform the trader of future potential predictions.

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline , some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed " neural networks "; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. Their main success came in the mids with the reinvention of backpropagation. Machine learning ML , reorganized as a separate field, started to flourish in the s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature.

It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.

As of , many sources continue to assert that machine learning remains a subfield of AI. Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of previously unknown properties in the data this is the analysis step of knowledge discovery in databases.

Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities which do often have separate conferences and separate journals, ECML PKDD being a major exception comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining KDD the key task is the discovery of previously unknown knowledge.

Evaluated with respect to known knowledge, an uninformed unsupervised method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Machine learning also has intimate ties to optimization : many learning problems are formulated as minimization of some loss function on a training set of examples.

Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples. The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.

Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample , while machine learning finds generalizable predictive patterns.

Jordan , the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, [34] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. A core objective of a learner is to generalize from its experience. The training examples come from some generally unknown probability distribution considered representative of the space of occurrences and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms.

Instead, probabilistic bounds on the performance are quite common. The bias—variance decomposition is one way to quantify generalization error. For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data.

If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time.

There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector , and the training data is represented by a matrix. Through iterative optimization of an objective function , supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.

An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Types of supervised learning algorithms include active learning , classification and regression. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are.

It has applications in ranking , recommendation systems , visual identity tracking, face verification, and speaker verification. Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized.

Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics , such as finding the probability density function.

Cluster analysis is the assignment of a set of observations into subsets called clusters so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness , or the similarity between members of the same cluster, and separation , the difference between clusters.

Other methods are based on estimated density and graph connectivity. Semi-supervised learning falls between unsupervised learning without any labeled training data and supervised learning with completely labeled training data. Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.

In weakly supervised learning , the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory , control theory , operations research , information theory , simulation-based optimization , multi-agent systems , swarm intelligence , statistics and genetic algorithms.

In machine learning, the environment is typically represented as a Markov decision process MDP. Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. Self-learning as a machine learning paradigm was introduced in along with a neural network capable of self-learning named crossbar adaptive array CAA.

The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions feelings about consequence situations. The system is driven by the interaction between cognition and emotion.

It is a system with only one input, situation s, and only one output, action or behavior a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value secondary reinforcement is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment.

After receiving the genome species vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations. Several learning algorithms aim at discovering better representations of the inputs provided during training. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions.

This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering , and allows a machine to both learn the features and use them to perform a specific task.

## Machine Learning A Probabilistic Approach

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The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in.

## Probabilistic machine learning and artificial intelligence

School of Computing , University of Utah. I obtained the Ph. My research interests mainly lie in probabilistic machine learning. My research include but are not limited to probabilistic graphical models, Bayesian deep learning, Bayesian nonparametric, physics informed machine learning, approximate inference, sparse learning, large-scale machine learning and kernel methods. From application side, I have developed probabilistic methods for microarray data analysis, association study for Alzheimer's disease, functional magnetic resonance imaging fMRI data analysis, and click-through-rate prediction for online advertising.

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Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied.

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