Bayesian networks phd thesis

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Bayesian networks phd thesis

The study of mathematical logic led directly to Alan Turing 's theory of computationwhich suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.

This insight, that digital computers can simulate any process of formal reasoning, is known as the Church—Turing thesis.

Uncertainty in Deep Learning (PhD Thesis) | Yarin Gal - Blog | Cambridge Machine Learning Group

Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation Progress slowed and inin response to the criticism of Sir James Lighthill [37] and ongoing pressure from the US Congress to fund more productive projects, both the U.

Bayesian networks phd thesis

The next few years would later Bayesian networks phd thesis called an " AI winter ", [9] a period when obtaining funding for AI projects was difficult. In the early s, AI research was revived by the commercial success of expert systems[38] a form of AI program that simulated the knowledge and analytical skills of human experts.

Bythe market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U. S and British governments to restore funding for academic research. According to Bloomberg's Jack Clark, was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in to more than 2, projects.

Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since Goals can be explicitly defined, or can be induced.

If the AI is programmed for " reinforcement learning ", goals can be implicitly induced by rewarding some types of behavior and punishing others. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A simple example of an algorithm is the following recipe for optimal play at tic-tac-toe: Otherwise, if a move "forks" to create two threats at once, play that move.

Otherwise, take the center square if it is free. Otherwise, if your opponent has played in a corner, take the opposite corner. Otherwise, take an empty corner if one exists.

Otherwise, take any empty square. Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics strategies, or "rules of thumb", that have worked well in the pastor can themselves write other algorithms.

Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any functionincluding whatever combination of mathematical functions would best describe the entire world.

These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of " combinatorial explosion ", where the amount of time needed to solve a problem grows exponentially.

Much of AI research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful. A second, more general, approach is Bayesian inference: The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies.

Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; [61] the best approach is often different depending on the problem.

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10, days, it will probably rise tomorrow morning as well".

Learners also work on the basis of " Occam's razor ": The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.

Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.

A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.

Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.

This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of " folk psychology " that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence".

A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.Aloha, I'm the chief scientist at benjaminpohle.comusly, I was an adjunct professor at Stanford's computer science department and the founder and CEO/CTO of MetaMind which was acquired by Salesforce in I enjoy improving the state of the art in AI through research (deep learning, natural language processing and computer vision) and making AI easily accessible to everyone.

The thesis is organized in two parts: the first part puts into context the findings of the PhD in an introductive review; the second part consists of the papers listed below. computaitonal methods. Hence, Bayesian Networks combine principles from graph theory, probability theory, computer science and statistics.

A Bayesian network is a directed acyclic graph (DAG) in which nodes represent random vari-ables. The structure of a DAG is defined by two sets, the set of nodes and the set of directed edges.

Bayesian Networks Phd Thesis

Thesis: Uncertainty in Deep Learning Some of the work in the thesis was previously presented in [ Gal, ; Gal and Ghahramani, a, b, c, d ; Gal et al., ], . Kevin Murphy's PhD Thesis "Dynamic Bayesian Networks: Representation, Inference and Learning" UC Berkeley, Computer Science Division, July "Modelling sequential data is important in many areas of science and engineering.

Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. Dear Twitpic Community - thank you for all the wonderful photos you have taken over the years. We have now placed Twitpic in an archived state.

On Bayesian Networks for Structural Health and Condition Monitoring - White Rose eTheses Online