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Generative Adversarial Networks (GAN) is a technique used to learn the distribution of some dataset in order to generate similar data. GAN models are notoriously difficult to train, which has caused limited deployment in the industry. The results of this study can be used to accelerate the process of making GANs production ready. An experiment was conducted where multiple GAN models were trained, with the hyperparameters Leaky ReLU alpha, convolutional filters, learning rate and batch size as independent variables. A Mann-Whitney U-test was used to compare the training time and training stability of each model to the others’. Except for the Leaky ReLU alpha, changes to the investigated hyperparameters had a significant effect on the training time and stability. This study is limited to a few hyperparameters and values, a single dataset and few data points, further research in the area could look at the generalisability of the results or investigate more hyperparameters.

[pt] Diferentes procedimentos têm sido propostos para a modelagem e previsão de séries temporais sendo que nos anos recentes muitos dos métodos mais importantes têm sido formulados na representação espaço de estado. A principal vantagem de tal abordagem é que se pode usar o Filtro de Kalman diretamente para, seqüencialmente, atualizar o vetor de estado. Apresentamos de forma sistemática a abordagem para a previsão de Séries Temporais não- Estacionárias formulada na representação de espaço de estado desenvolvida por P.Young. A novidade desta abordagem não está na natureza dos algoritmos recursivos, e sim na maneira como os hiperparâmetros são obtidos. Modelling and forecasting of Time Series have been approached in many different ways. Lately, the most important approaches have been formulated in a state space framework. The state space representation enables the state vector to be sequentially updated in time via the Kalman filter. In this dissertation, we present in a systematic way an approach to modelling and forecasting of non-stationary time series, formulated in state space terms, and due to P. Young. The novelty of this methodology is neither the nature fo the time series models nor the recursive algorithms, but on how the hyperparameters are estimated / [en] Modelling and forecasting of times Series have been approached in many different ways. Lately, the most important approaches have been formulated in a space framework. The state space representation enables the state vector to be sequencially updated in time via the Kalman filter. In this dissertation, we present in a systematic way an approach to modelling and forecasting of non-stationary time series, formulated in state space terms, and due to P. Young. The novelty of this methodology is neither the nature of the time series models nor the recursive algorithms, but on how the hyperparameteres are estimated

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

2D to 3D reconstruction is an ill-posed problem in the field of Autonomous Robot Navigation. Many practitioners are tend to utilize the enormous success of Deep Learning techniques like CNN, ANN etc to solve tasks related to this 2D to 3D reconstruction. Generally, every deep learning model involves implementation of different optimizers related to the tasks to lower the possible negativity in its results and selection of hyper parameter values for these optimizers during the process of training the model with required dataset.Selection of this optimizer hyper-parameters requires in-depth knowledge and trials and errors. So proposing optimal hyper parameters for optimizers results in no waste in computational resources and time.Hence solution for the selected task cab found easily. The main objective of this research is to propose optimal hyper parameter values of various deep learning optimizers related to 2D to 3D reconstruction and proposing best optimizer among them in terms of computational time and resources To achieve the goal of this study two research methods are used in our work. The first one is a Systematic Literature Review; whose main goal is to reveal the widely selected and used optimizers for 2D to 3D reconstruction model using 3D Deep Learning techniques.The second, an experimental methodology is deployed, whose main goal is to propose the optimal hyper parameter values for respective optimizers like Adam, SGD+Momentum, Adagrad, Adadelta and Adamax which are used in 3D reconstruction models. In case of the computational time, Adamax optimizer outperformed all other optimizers used with training time (1970min), testing time (3360 min), evaluation-1 (16 min) and evaluation-2 (14 min).In case of Average Point cloud points, Adamax outperformed all other optimizers used with Mean value of 28451.04.In case of pred->GT and GT->pred values , Adamax optimizer outperformed all other optimizers with mean values of 4.742 and 4.600 respectively. Point Cloud Images with respective dense cloud points are obtained as results of our experiment.From the above results,Adamax optimizer is proved to be best in terms of visualization of Point Cloud images with optimal hyper parameter values as below:Epochs : 1000    Learning Rate : 1e-2    Chunk size : 32    Batch size : 32.  In this study,'Adamax' optimizer with optimal hyper para meter values and better Point Cloud Image is proven to be the best optimizer that can be used in a 2D to 3D reconstruction related task that deals with Point Cloud images

Machine learning and image recognition is a big and growing subject in today's society. Therefore the aim of this thesis is to compare convolutional neural networks with different hyperparameter settings and see how the hyperparameters affect the networks test accuracy in identifying images of traffic signs. The reason why traffic signs are chosen as objects to evaluate hyperparameters is due to the author's previous experience in the domain. The object itself that is used for image recognition does not matter. Any dataset with images can be used to see the hyperparameters affect. Grid search is used to create a large amount of models with different width and depth, learning rate and momentum. Convolution layers, activation functions and batch size are all tested separately. These experiments make it possible to evaluate how the hyperparameters affect the networks in their performance of recognizing images of traffic signs. The models are created using Keras API and then trained and tested on the dataset Traffic Signs Preprocessed. The results show that hyperparameters affect test accuracy, some affect more than others. Configuring learning rate and momentum can in some cases result in disastrous results if they are set too high or too low. Activation function also show to be a crucial hyperparameter where it in some cases produce terrible results.

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Rapporten beskriver utvecklingsarbetet och resultatet från utvecklingen av en robotplattform vid namn AIDA (AI Design Assistant), som utvecklades åt Institutionen för datavetenskap vid Linköpings universitet. Plattformen består av en robotarm som utgörs av sex stycken servomotorer, som är anslutna till en enkortsdator. En Android-surfplatta sitter integrerad på robotarmen och har en applikation installerad som utgör användargränssnittet. Tre huvudsakliga funktioner för plattformen utvecklades. Dessa funktioner är objektigenkänning, objektspårning och taligenkänning. Objektigenkänningen kan klassificera fyra olika fruktsorter, objektspårningen kan spåra objekt och följa dem med robotarmen genom inverskinematik, och taligenkänningen kan transkribera tal till text och svara på kommandon. Utifrån resultatet och diskussionen härleds slutsatser över fyra frågeställningar relaterade till utvecklingsarbetet. Projektet utfördes som en del av kursen TDDD96 Kandidatprojekt i programvaruutveckling, och varje projektmedlem har även skrivit ett individuellt bidrag till rapporten som behandlar områden kopplade till projektarbetet. / This report describes the development process and the resulting product from the development of a robot platform named AIDA (AI Design Assistant), that was developed on a request from the Department of Computer and Information Science at Linköping University. The platform consists of a robot arm that is made up by six servo motors connected to a single-board computer. An Android tablet is attached to the robot arm and has an application installed which constitutes the user interface. Three main functions were developed for the platform. These functions constitute object recognition, object tracking, and speech recognition. The object recognition module can classify four different types of fruit, the object tracking module can track objects and follow them by moving the robot arm using inverse kinematics, and the speech recognition module can transcribe speech to text and respond to  audible commands. Conclusions over four questions related to the development of the product are derived from the results and discussion chapters of the report. The project was conducted as a part of the course TDDD96 Software Engineering – Bachelor Project, and each project member has produced an individual contribution to the report which covers subjects related to the project.

The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyperparameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of implementation of the unary classifier.