Chat-bots are computer programs designed to conduct textual or audible conversations with a single user. The job of a chat-bot is to be able to find the best response for any request the user issues. The best response is considered to answer the question and contain relevant information while following grammatical and lexical rules. Modern chat-bots often have trouble accomplishing all these tasks. State-of-the-art approaches, such as deep learning, and large datasets help chat-bots tackle this problem better. While there is a number of different approaches that can be applied for different kind of bots, datasets of suitable size are not always available. In this work, we introduce and evaluate a method of expanding the size of datasets. This will allow chat-bots, in combination with a good learning algorithm, to achieve higher precision while handling their tasks. The expansion method uses the continual learning approach that allows the bot to expand its own dataset while holding conversations with its users. In this work we test continual learning with IBM Watson Assistant chat-bot as well as a custom case study chat-bot implementation. We conduct the testing using a smaller and a larger datasets to find out if continual learning stays effective as the dataset size increases. The results show that the more conversations the chat-bot holds, the better it gets at guessing the intent of the user. They also show that continual learning works well for larger and smaller datasets, but the effect depends on the specifics of the chat-bot implementation. While continual learning makes good results better, it also turns bad results into worse ones, thus the chat-bot should be manually calibrated should the precision of the original results, measured before the expansion, decrease.

The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.

Hra Fantasy Premier League poskytuje miliónom hráčov po celom svete možnosť stať sa na chvíľu manažérom svojho vlastného klubu. Výsledky a bodové ohodnotenie v hre závisia na správnom predvídaní, ako sa budú hráči chovať v skutočných futbalových zápasoch. Ak by pri tomto rozhodovaní pomáhal software na predikciu a analýzu budúcich výkonov hráčov, výsledky v hre sa môžu rapídne zlepšiť. Táto diplomová práca sa zaoberá návrhom a implementáciou predikčného modelu, ktorý využíva neurónové siete na predikcie časových radov počas celej sezóny v hre. Boli použité metódy na spracovanie dát o hráčoch a kluboch za posledné 4 sezóny. Výkonnosť a presnosť predikčných metód boli testované na dátach z poslednej sezóny Premier League a predikcie algoritmu sa vo väčšine prípadov blížili realite. Ak by sa užívateľ držal predikčného modelu v hre stopercentne, získal by väčší počet bodov ako bežný hráč, ktorý žiadny predikčný model nepoužíva.

This work deals with the creation of a system that allows uploading and annotating scans of historical documents and subsequent active learning of models for character recognition (OCR) on available annotations (marked lines and their transcripts). The work describes the process, classifies the techniques and presents an existing system for character recognition. Above all, emphasis is placed on machine learning methods. Furthermore, the methods of active learning are explained and a method of active learning of available OCR models from annotated scans is proposed. The rest of the work deals with a system design, implementation, available datasets, evaluation of self-created OCR model and testing of the entire system.

La plupart des méthodes de traitement automatique des langues (TAL) peuvent être formalisées comme des problèmes de prédiction, dans lesquels on cherche à choisir automatiquement l'hypothèse la plus plausible parmi un très grand nombre de candidats. Malgré de nombreux travaux qui ont permis de mieux prendre en compte la structure de l'ensemble des hypothèses, la taille de l'espace de recherche est généralement trop grande pour permettre son exploration exhaustive. Dans ce travail, nous nous intéressons à l'importance du design de l'espace de recherche et étudions l'utilisation de contraintes pour en réduire la taille et la complexité. Nous nous appuyons sur l'étude de trois problèmes linguistiques — l'analyse morpho-syntaxique, le transfert cross-lingue et le problème du réordonnancement en traduction — pour mettre en lumière les risques, les avantages et les enjeux du choix de l'espace de recherche dans les problèmes de TAL.Par exemple, lorsque l'on dispose d'informations a priori sur les sorties possibles d'un problème d'apprentissage structuré, il semble naturel de les inclure dans le processus de modélisation pour réduire l'espace de recherche et ainsi permettre une accélération des traitements lors de la phase d'apprentissage. Une étude de cas sur les modèles exponentiels pour l'analyse morpho-syntaxique montre paradoxalement que cela peut conduire à d'importantes dégradations des résultats, et cela même quand les contraintes associées sont pertinentes. Parallèlement, nous considérons l'utilisation de ce type de contraintes pour généraliser le problème de l'apprentissage supervisé au cas où l'on ne dispose que d'informations partielles et incomplètes lors de l'apprentissage, qui apparaît par exemple lors du transfert cross-lingue d'annotations. Nous étudions deux méthodes d'apprentissage faiblement supervisé, que nous formalisons dans le cadre de l'apprentissage ambigu, appliquées à l'analyse morpho-syntaxiques de langues peu dotées en ressources linguistiques.Enfin, nous nous intéressons au design de l'espace de recherche en traduction automatique. Les divergences dans l'ordre des mots lors du processus de traduction posent un problème combinatoire difficile. En effet, il n'est pas possible de considérer l'ensemble factoriel de tous les réordonnancements possibles, et des contraintes sur les permutations s'avèrent nécessaires. Nous comparons différents jeux de contraintes et explorons l'importance de l'espace de réordonnancement dans les performances globales d'un système de traduction. Si un meilleur design permet d'obtenir de meilleurs résultats, nous montrons cependant que la marge d'amélioration se situe principalement dans l'évaluation des réordonnancements plutôt que dans la qualité de l'espace de recherche. / Most natural language processing tasks are modeled as prediction problems where one aims at finding the best scoring hypothesis from a very large pool of possible outputs. Even if algorithms are designed to leverage some kind of structure, the output space is often too large to be searched exaustively. This work aims at understanding the importance of the search space and the possible use of constraints to reduce it in size and complexity. We report in this thesis three case studies which highlight the risk and benefits of manipulating the seach space in learning and inference.When information about the possible outputs of a sequence labeling task is available, it may seem appropriate to include this knowledge into the system, so as to facilitate and speed-up learning and inference. A case study on type constraints for CRFs however shows that using such constraints at training time is likely to drastically reduce performance, even when these constraints are both correct and useful at decoding.On the other side, we also consider possible relaxations of the supervision space, as in the case of learning with latent variables, or when only partial supervision is available, which we cast as ambiguous learning. Such weakly supervised methods, together with cross-lingual transfer and dictionary crawling techniques, allow us to develop natural language processing tools for under-resourced languages. Word order differences between languages pose several combinatorial challenges to machine translation and the constraints on word reorderings have a great impact on the set of potential translations that is explored during search. We study reordering constraints that allow to restrict the factorial space of permutations and explore the impact of the reordering search space design on machine translation performance. However, we show that even though it might be desirable to design better reordering spaces, model and search errors seem yet to be the most important issues.

This thesis proposes a general approach to solve the offline flight-maneuver identification problem using machine learning methods. The purpose of the study was to provide means for the aircraft professionals at the flight test and verification department of Saab Aeronautics to automate the procedure of analyzing flight test data. The suggested approach succeeded in generating binary classifiers and multiclass classifiers that identified six flight maneuvers of different complexity from real flight test data. The binary classifiers solved the problem of identifying one maneuver from flight test data at a time, while the multiclass classifiers solved the problem of identifying several maneuvers from flight test data simultaneously. To achieve these results, the difficulties that this time series classification problem entailed were simplified by using different strategies. One strategy was to develop a maneuver extraction algorithm that used handcrafted rules. Another strategy was to represent the time series data by statistical measures. There was also an issue of an imbalanced dataset, where one class far outweighed others in number of samples. This was solved by using a modified oversampling method on the dataset that was used for training. Logistic Regression, Support Vector Machines with both linear and nonlinear kernels, and Artifical Neural Networks were explored, where the hyperparameters for each machine learning algorithm were chosen during model estimation by 4-fold cross-validation and solving an optimization problem based on important performance metrics. A feature selection algorithm was also used during model estimation to evaluate how the performance changes depending on how many features were used. The machine learning models were then evaluated on test data consisting of 24 flight tests. The results given by the test data set showed that the simplifications done were reasonable, but the maneuver extraction algorithm could sometimes fail. Some maneuvers were easier to identify than others and the linear machine learning models resulted in a poor fit to the more complex classes. In conclusion, both binary classifiers and multiclass classifiers could be used to solve the flight maneuver identification problem, and solving a hyperparameter optimization problem boosted the performance of the finalized models. Nonlinear classifiers performed the best on average across all explored maneuvers.

Recurrent Neural Networks(RNNs) are powerful models that have obtained outstanding achievements in many sequence learning tasks. Despite their accomplishments, RNN models still suffer with long sequences during training. It is because error propagate backwards from output to input layers carrying gradient signals, and with long input sequence, issues like vanishing and exploding gradients can arise. This thesis reviews many current studies and existing architectures designed to circumvent the long-term dependency problems in backpropagation through time (BPTT). Mainly, we focus on the method proposed by Trinh et al. (2018) which uses semi- supervised learning method to alleviate the long-term dependency problems in BPTT. Despite the good results Trinh et al. (2018)’s model achieved, we suggest that the model can be further improved with a more systematic way of balancing auxiliary signals. In this thesis, we present our paper – RNNs with Private and Shared Representations for Semi-Supervised Learning – which is currently under review for AAAI-2019. We propose a semi-supervised RNN architecture with explicitly designed private and shared representations that regulates the gradient flow from auxiliary task to main task. / Les réseaux neuronaux récurrents (RNN) sont des modèles puissants qui ont obtenu des réalisations exceptionnelles dans de nombreuses tâches d’apprentissage séquentiel. Malgré leurs réalisations, les modèles RNN sou˙rent encore de longues séquences pendant l’entraî-nement. C’est parce que l’erreur se propage en arrière de la sortie vers les couches d’entrée transportant des signaux de gradient, et avec une longue séquence d’entrée, des problèmes comme la disparition et l’explosion des gradients peuvent survenir. Cette thèse passe en revue de nombreuses études actuelles et architectures existantes conçues pour contour-ner les problèmes de dépendance à long terme de la rétropropagation dans le temps (BPTT). Nous nous concentrons principalement sur la méthode proposée par cite Trinh2018 qui utilise une méthode d’apprentissage semi-supervisée pour atténuer les problèmes de dépendance à long terme dans BPTT. Malgré les bons résultats obtenus avec le modèle de cite Trinh2018, nous suggérons que le modèle peut être encore amélioré avec une manière plus systématique d’équilibrer les signaux auxiliaires. Dans cette thèse, nous présentons notre article - emph RNNs with Private and Shared Representations for Semi-Supervised Learning - qui est actuellement en cours de révision pour AAAI-2019. Nous propo-sons une architecture RNN semi-supervisée avec des représentations privées et partagées explicitement conçues qui régule le flux de gradient de la tâche auxiliaire à la tâche principale.

Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The image retrieval is achieved by transforming images to feature representations in a latent space, where it is possible to reason about image similarity in terms of image content. In this thesis, a self-supervised method is developed to learn feature representations of road scenes images. The self-supervised method learns feature representations for images by adapting intermediate convolutional features from an existing deep Convolutional Neural Network (CNN). A contrastive approach based on Noise Contrastive Estimation (NCE) is used to train the feature learning model. For complex images like road scenes where mutiple image aspects can occur simultaneously, it is important to embed all the salient image aspects in the feature representation. To achieve this, the output feature representation is obtained as an ensemble of feature embeddings which are learned by focusing on different image aspects. An attention mechanism is incorporated to encourage each ensemble member to focus on different image aspects. For comparison, a self-supervised model without attention is considered and a simple dimensionality reduction approach using SVD is treated as the baseline. The methods are evaluated on nine different evaluation datasets using CBIR performance metrics. The datasets correspond to different image aspects and concern the images at different spatial levels - global, semi-global and local. The feature representations learned by self-supervised methods are shown to perform better than the SVD approach. Taking into account that no labelled data is required for training, learning representations for road scenes images using self-supervised methods appear to be a promising direction. Usage of multiple query images to emphasize a query intention is investigated and a clear improvement in CBIR performance is observed. It is inconclusive whether the addition of an attentive mechanism impacts CBIR performance. The attention method shows some positive signs based on qualitative analysis and also performs better than other methods for one of the evaluation datasets containing a local aspect. This method for learning feature representations is promising but requires further research involving more diverse and complex image aspects.

Supervised learning requires labeled data which is cumbersome to produce, making it costly and time-consuming. SimCLR is a self-supervising framework that uses data augmentations to learn without labels. This thesis investigates how well cropping and color distorting augmentations work for two datasets, MPI3D and Causal3DIdent. The representations learned are evaluated using representation similarity analysis. The data augmentations were meant to make the model learn invariant representations of the object shape in the images regarding it as content while ignoring unnecessary features and regarding them as style. As a result, 8 models were created, models A-H. A and E were trained using supervised learning as a benchmark for the remaining self-supervised models. B and C learned invariant features of style instead of learning invariant representations of shape. Model D learned invariant representations of shape. Although, it also regarded style-related factors as content. Model F, G, and H managed to learn invariant representations of shape with varying intensities while regarding the rest of the features as style. The conclusion was that models can learn invariant representations of features related to content using self-supervised learning with the chosen augmentations. However, the augmentation settings must be suitable for the dataset. / Övervakad maskininlärning kräver annoterad data, vilket är dyrt och tidskrävande att producera. SimCLR är ett självövervakande maskininlärningsramverk som använder datamodifieringar för att lära sig utan annoteringar. Detta examensarbete utvärderar hur väl beskärning och färgförvrängande datamodifieringar fungerar för två dataset, MPI3D och Causal3DIdent. De inlärda representationerna utvärderas med hjälp av representativ likhetsanalys. Syftet med examensarbetet var att få de självövervakande maskininlärningsmodellerna att lära sig oföränderliga representationer av objektet i bilderna. Meningen med datamodifieringarna var att påverka modellens lärande så att modellen tolkar objektets form som relevant innehåll, men resterande egenskaper som icke-relevant innehåll. Åtta modeller skapades (A-H). A och E tränades med övervakad inlärning och användes som riktmärke för de självövervakade modellerna. B och C lärde sig oföränderliga representationer som bör ha betraktas som irrelevant istället för att lära sig form. Modell D lärde sig oföränderliga representationer av form men också irrelevanta representationer. Modellerna F, G och H lyckades lära sig oföränderliga representationer av form med varierande intensitet, samtidigt som de resterande egenskaperna betraktades som irrelevant. Beskärning och färgförvrängande datamodifieringarna gör således att självövervakande modeller kan lära sig oföränderliga representationer av egenskaper relaterade till relevant innehåll. Specifika inställningar för datamodifieringar måste dock vara lämpliga för datasetet.

Advances in machine learning, together with an abundance of available data has lead to an explosion in personalized offerings and being able to predict what consumers want, and need without them having to ask for it. During the last decade, it has become a multi billion dollar industry, and a capability upon many of the leading tech companies rely on in their business model. Indeed, in today's business world, it is not only a capability for competitive advantage, but in many cases a matter of survival. This thesis aims to create a machine learning model able to predict customers interested in an upselling opportunity of changing their payment method after completing a purchase with the Swedish payment solutions company, Klarna Bank. Hence, the overall aim is to personalize the customer experience on the confirmation page. Two gradient boosting methods and one deep learning method were trained, evaluated and compared for this task. A logistic regression model was also trained and used as a baseline model. The results showed that all models performed better than the baseline model, with the gradient boosting methods showing the best performance. All of the models were also able to outperform the current solution with no personalization, with the best model reducing the amount of false positives by 50%. / Tillgång till stora datamängder har tillsammans med framsteg inom maskininlärning resulterat i en explotionsartad ökning i personifierade erbjudanden och möjligheter att förutspå kunders behov. Det har under det senaste decenniet utvecklats till en multimiljardindustri och en förmåga som många av de ledande techbolagen i världen förlitar sig på i sina verksamheter. I många fall är det till och med en förutsättning för att överleva i dagens industrilandskap. Det här examensarbetet ämnar att skapa en maskininlärningsmodell som är kapabel till att förutspå kunders intresse för att "uppgradera" sin betalmetod efter ett slutfört köp med den svenska betallösningsföretaget Klarna Bank. Konceptet att erbjuda en kund att uppgradera en redan vald produkt eller tjänst är på engelska känt som upselling. Det övergripande syftet för detta projekt är därför att skapa en personifierad kundupplevelse på Klarnas bekräftelsesida. Följaktligen implementerades och utvärderades två så kallade gradient boosting - metoder samt en djupinlärningsmetod. Vidare implementerades även en logistisk regressionsmodell som basmodell för att jämföra de övriga modeller med. Resultaten visar hur alla modeller överträffade den tillämpade basmodellen, där gradient boosting-metoderna påvisade bättre resultat än djupinlärningsmetoden. Därtill visar alla modeller en förbättring i jämförelse med dagens lösning på Klarnas bekräftelssesida, utan personifiering, där den bästa modellen förbättrade utfallet med 50%.