In light of the conceptual ambiguity and rhetoric surrounding the term "learning community", this thesis elaborates conceptual foundations for the term. Drawing upon sociological and anthropological literature, the thesis presents community in structural-functional and psycho-social dimensions. In the relationship between these dimensions, the former is argued as both the medium and the milieu Jun 25, · These are common requests from the Master Thesis On Learning Design students, who do not know how to manage the tasks on time and wish to have more leisure hours as the college studies progress. However, the writing agency has found a perfect solution for the issue that has been bothering the Master Thesis On Learning Design students Master Thesis On Learning Design in the English /10() Master Thesis On Learning Design, Essay On My Future Plans, Shapes In English For Vowels In Esl Class, Cheap Reflective Essay Proofreading Service For University Math Algebra Accounting Geography Days of stress are over now because our professionals would help you at every phase and extend professional assistance in completing your law assignments
Available Master's thesis topics in machine learning | Machine Learning | UiB
Most learning and inference tasks with Bayesian networks are NP-hard, master thesis in learning design. Therefore, one often resorts to using different heuristics that do not give any quality guarantees. Traditionally, probabilistic graphical models use a graph structure to represent dependencies and independencies between random variables.
Sum-product networks are a relatively new type of a graphical model where the graphical structure models computations and not the relationships between variables. The benefit of this representation is that inference computing conditional probabilities can be done in linear time with respect to the size of the network. Potential thesis topics in this area: a Compare inference speed with sum-product networks and Bayesian networks. Characterize situations when one model is better than the other, master thesis in learning design.
b Learning the sum-product networks is done using heuristic algorithms. What is the effect of approximation in practice? The naming of Bayesian networks is somewhat misleading because there is nothing Bayesian in them per se; A Bayesian network is just a representation of a joint probability distribution. One can, of course, use a Bayesian network while doing Bayesian inference.
One can also learn Bayesian networks in a Bayesian way. That is, instead of finding an optimal network one computes the posterior distribution over networks.
Task: Develop algorithms for Bayesian learning of Bayesian networks e, master thesis in learning design. The idea behind matrix factorization is to represent a large data matrix as a product of two or more smaller matrices.
They are often used in, for example, dimensionality reduction and recommendation systems. Probabilistic matrix factorization methods can be used to quantify uncertainty in recommendations.
However, large-scale probabilistic matrix factorization is computationally challenging. Potential thesis topics in this area: a Develop scalable methods for large-scale matrix factorization non-probabilistic or probabilisticb Develop probabilistic methods for implicit feedback e. Standard deep neural networks do not quantify uncertainty in predictions. On the other hand, Bayesian methods provide a principled way to handle uncertainty. Combining these approaches leads to Bayesian neural networks.
The challenge is that Bayesian neural networks can be cumbersome to use and difficult to learn. The task is to analyze Bayesian neural networks and different inference algorithms in some simple setting.
Deep learning is usually applied in regression or classification problems. However, there has been some recent work on using deep learning to develop heuristics for master thesis in learning design optimization problems; see, e.
Task: Choose a combinatorial problem or several related problems and develop deep learning methods to solve them. References: [1] Vinyals, Fortunato and Jaitly: Pointer networks.
NIPS Advisors: Pekka Parviainen, Ahmad Hemmati. Mode seeking considers estimating the number of local maxima of a function f. Sometimes one can find modes by, e. However, often the function is unknown and we have only access to some possibly noisy values of the function. In topological data analysis, we can analyze topological structures using persistent homologies. the birth and death of connected topological components as we expand the space around each point where we have observed our function.
These observations turn out to be closely related to the modes local maxima of the function. A recent paper [1] proposed an efficient method for mode seeking. In this project, the task is to extend the ideas from [1] to get a probabilistic estimate on the number of modes. To this end, one has to use probabilistic methods such as Gaussian processes. Bauer, A. Munk, H. Sieling, and M. Persistence barcodes versus Kolmogorov signatures: Detecting modes of one-dimensional signals.
Foundations of computational mathematics - 33, Advisors: Pekka ParviainenNello Blaser. Isomap is a non-linear dimensionality reduction method with two free hyperparameters number of nearest neighbors and neighborhood radius. Different hyperparameters result in dramatically different embeddings. Previous methods for selecting hyperparameters focused on choosing one optimal hyperparameter. In this project, you will explore the use of persistent homology to find parameter ranges that result in stable embeddings.
The project has theoretic and computational aspects. Finding cycles in directed graphs is one of the subroutines in many algorithms for learning the structure of Bayesian networks. In this project, you will use methods from topological data analysis on directed graphs to find cycles more efficiently, master thesis in learning design.
Standard tools for finding cycles exist in the case of undirected graphs, and some recent work has focused on finding persistent homology of directed graphs. In this project, you will combine the two approaches to implement a method that finds cycles in directed graphs. You will then compare these methods with standard network methods in the context of Bayesian networks.
This is an implementation project. In topological data analysis, the term stability usually means that the output of an algorithm changes little, when the input is perturbed. In computational learning theory on the other hand, there are numerous definitions of stability, such as hypothesis stability, error stability or uniform stability.
In this project, you will relate different definitions of stability to one-another, learn about stability of particular machine learning algorithms and develop the stability theory for persistent homology from a computational learning theory standpoint. This project is mostly theoretical. Persistent homology is a generalization of hierarchical clustering to find more structure than just the clusters, master thesis in learning design.
Traditionally, hierarchical clustering has been evaluated using resampling methods and assessing stability properties. In this project you will generalize master thesis in learning design resampling methods to develop novel stability properties that can be used to assess persistent homology. This project has theoretic and computational aspects, master thesis in learning design. Persistent homology is becoming a standard method for analyzing data.
In this project, you will to generate benchmark data sets for testing different aspects of the persistence pipeline. You will generate benchmarks for different objectives, such as data with known persistence diagram, where for example bottleneck distance can be minimized and data with classification and regression targets.
Data master thesis in learning design will be sampled from a manifold master thesis in learning design or without noise or from a general probability distribution.
This project is mostly computational. Divisive covers are a divisive technique for generating filtered simplicial complexes. They original used a naive way of dividing data into a cover. In this project, you will explore different methods of dividing space, based on principle component analysis, support vector machines and k-means clustering. In addition, you will explore methods of using divisive covers for classification.
This project will be mostly computational. Binarized neural networks BNNs have recently attracted a lot of attention in the AI research community as a memory-efficient alternative to classical deep neural network models.
InNarodytska et al. proposed an exact translation of BNNs into propositional logic. Using this translation, various properties such as robustness against adversarial attacks can be proved. The main tasks in this project are to study BNNs and the translation into propositional logic, implement an optimised version of the translation, and perform experiments verifying its correctness.
Master thesis in learning design neural networks by Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv,Yoshua Bengio NeurIPS Verifying Properties of Binarized Deep Neural Networks by Nina Narodytska, Shiva PrasadKasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, Toby Walsh AAAI Quantum computers can solve certain types of problems exponentially faster than classical computers - so-called quantum supremacy.
However, it is still mostly unclear how far quantum supremacy goes, i. for what types of problems quantum computing outperforms classical computing. As quantum computers become larger more qubits and more reliable lower error rateswe approach the point where they may become relevant for machine learning applications. One of the proposed methods in this field are so-called quantum neural networks QNN.
Where classical neural networks CNN use real-valued weights, activation functions, input and output data, in a QNN all of these are represented by complex quantum states and quantum operations.
This allows for a much denser encoding of information, master thesis in learning design, so that a small QNN may be functionally equivalent to a much larger CNN. For larger QNN, the equivalent CNN would have to be so enormously large that it is completely infeasible. This leads to the central objective of this project:Under which conditions can a QNN achieve quantum supremacy?
How do QNN and CNN compare in terms of learning speed, accuracy, etc. for different classes of problems, and how does their performance scale with size? In the foreseeable future, quantum computers will be relatively noisy; that means they will have high error rates. This poses an additional problem:How does noise affect the performance of a QNN? Are there limits to how much noise a QNN can tolerate?
How does the effect of noise scale with the size of the QNN? You can approach this project in two ways:. If you are interested, please contact Philip Turk or Ana Ozaki. et al.
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In light of the conceptual ambiguity and rhetoric surrounding the term "learning community", this thesis elaborates conceptual foundations for the term. Drawing upon sociological and anthropological literature, the thesis presents community in structural-functional and psycho-social dimensions. In the relationship between these dimensions, the former is argued as both the medium and the milieu Master Thesis In Learning Design that only a professional writer can create academic content that is perfect and that obtains the best blogger.com online essay writers in our network have a strong track record of providing research and writing assistance to students Our experts are Master Thesis In Learning Design available 24/7 to help customers send their jobs on time, even if they only have 12 hours left before the deadline.. According to a recent survey, 94% of all copies ordered from our professionals will be delivered before the deadline
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