In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.

<p>The aim of this study has been to investigate why we saw the formation of the green party and junilistan in Swedish politics. The questions being asked is why the individuals behind the party decided to take the step towards forming a new party as well as why they felt it worth forming it.</p><p>The theories being used is the sequential model created by Gissur Ó Erlingsson, which is complemented with theoretical assumptions by Paul Lucardie. The sequential model illustrates the party forming processes in three steps, which then through process-tracing allows for the identification of events that have exerted an influence on the entrepreneur to decide taking the step towards forming a new party. With the help of Lucardie I presented assumptions which gave me possibility to investigate why they then felt it worth forming the new party.</p><p>The conclusion showed that the step toward party formation in both cases occurred when the entrepreneurs experienced that their demands where not met. The second question was divided in to two sub questions which showed that the entrepreneurs in both cases made the calculation that a new party would find support amongst the electorate but regarding the ability to finance and attach competence the result was more diverging.</p>

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Deep learning models have been used widely in various areas and applications of our everyday lives. They could also change the way non-profit organizations work and help optimize fundraising results. In this thesis, sequential models are applied in fundraising to compare their performance against the traditional machine learning model. Sequential model is a type of neural network that is specialized for processing sequential data. Although some research utilizing machine learning algorithms in fundraising context exists, it is based on the data extracted from the specific time window, which does not take time-dependency of features into account; therefore, time-series features are independent at each data point relative to others. This approach results in loss of time notion. In this thesis, we experiment with the application of time-dependent sequential models including Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and their variants in the fundraising domain to predict the alumni monetary contribution to the university. We also expand our study by including the architecture that treats time-invariant demographic data as a condition to the sequential layers. In this model, the time-dependent data is concatenated after running the sequential model. Sequential deep learning is empirically evaluated and compared against the traditional machine learning models. The results demonstrate the potential use of both traditional machine learning and sequential deep learning in the prediction of fundraising outcomes and offer non-profit organizations solutions to achieve their mission. / Graduate

Les algorithmes d'inférence ou d'optimisation possèdent généralement des hyperparamètres qu'il est nécessaire d'ajuster. Nous nous intéressons ici à l'automatisation de cette étape d'ajustement et considérons différentes méthodes qui y parviennent en apprenant en ligne la structure du problème considéré.La première moitié de cette thèse explore l'ajustement des hyperparamètres en apprentissage artificiel. Après avoir présenté et amélioré le cadre générique de l'optimisation séquentielle à base de modèles (SMBO), nous montrons que SMBO s'applique avec succès à l'ajustement des hyperparamètres de réseaux de neurones profonds. Nous proposons ensuite un algorithme collaboratif d'ajustement qui mime la mémoire qu'ont les humains d'expériences passées avec le même algorithme sur d'autres données.La seconde moitié de cette thèse porte sur les algorithmes MCMC adaptatifs, des algorithmes d'échantillonnage qui explorent des distributions de probabilité souvent complexes en ajustant leurs paramètres internes en ligne. Pour motiver leur étude, nous décrivons d'abord l'observatoire Pierre Auger, une expérience de physique des particules dédiée à l'étude des rayons cosmiques. Nous proposons une première partie du modèle génératif d'Auger et introduisons une procédure d'inférence des paramètres individuels de chaque événement d'Auger qui ne requiert que ce premier modèle. Ensuite, nous remarquons que ce modèle est sujet à un problème connu sous le nom de label switching. Après avoir présenté les solutions existantes, nous proposons AMOR, le premier algorithme MCMC adaptatif doté d'un réétiquetage en ligne qui résout le label switching. Nous présentons une étude empirique et des résultats théoriques de consistance d'AMOR, qui mettent en lumière des liens entre le réétiquetage et la quantification vectorielle / Inference and optimization algorithms usually have hyperparameters that require to be tuned in order to achieve efficiency. We consider here different approaches to efficiently automatize the hyperparameter tuning step by learning online the structure of the addressed problem. The first half of this thesis is devoted to hyperparameter tuning in machine learning. After presenting and improving the generic sequential model-based optimization (SMBO) framework, we show that SMBO successfully applies to the task of tuning the numerous hyperparameters of deep belief networks. We then propose an algorithm that performs tuning across datasets, mimicking the memory that humans have of past experiments with the same algorithm on different datasets. The second half of this thesis deals with adaptive Markov chain Monte Carlo (MCMC) algorithms, sampling-based algorithms that explore complex probability distributions while self-tuning their internal parameters on the fly. We start by describing the Pierre Auger observatory, a large-scale particle physics experiment dedicated to the observation of atmospheric showers triggered by cosmic rays. The models involved in the analysis of Auger data motivated our study of adaptive MCMC. We derive the first part of the Auger generative model and introduce a procedure to perform inference on shower parameters that requires only this bottom part. Our model inherently suffers from label switching, a common difficulty in MCMC inference, which makes marginal inference useless because of redundant modes of the target distribution. After reviewing existing solutions to label switching, we propose AMOR, the first adaptive MCMC algorithm with online relabeling. We give both an empirical and theoretical study of AMOR, unveiling interesting links between relabeling algorithms and vector quantization.

The aim of this study has been to investigate why we saw the formation of the green party and junilistan in Swedish politics. The questions being asked is why the individuals behind the party decided to take the step towards forming a new party as well as why they felt it worth forming it. The theories being used is the sequential model created by Gissur Ó Erlingsson, which is complemented with theoretical assumptions by Paul Lucardie. The sequential model illustrates the party forming processes in three steps, which then through process-tracing allows for the identification of events that have exerted an influence on the entrepreneur to decide taking the step towards forming a new party. With the help of Lucardie I presented assumptions which gave me possibility to investigate why they then felt it worth forming the new party. The conclusion showed that the step toward party formation in both cases occurred when the entrepreneurs experienced that their demands where not met. The second question was divided in to two sub questions which showed that the entrepreneurs in both cases made the calculation that a new party would find support amongst the electorate but regarding the ability to finance and attach competence the result was more diverging.

Transformer models, delivering big improvement in AI text-models (NLP), are now being applied in Knowledge Tracing to track the knowledge of students over time. One of the first, SAINT, showed quite some improvement over the then SOTA results on the public EdNet dataset and caused an increase in research based on transformer-based models. In this paper, we firstly aim to reproduce the SAINT results on the EdNet dataset but are unable to report a similar performance as the original paper. This might be due to implementation details, which we were not able to completely reconstruct. We hope to pave the road for further reproducibility, as an increasingly important part of AI research. Furthermore, we apply the model to a company dataset much larger than any public dataset (more interactions, more exercises and more skills). Such a dataset is on the one hand more challenging (more skills mixed), and on the other hand, provides much more data (which should help our models). We compare the SAINT model and the seminal IRT model, and find that the SAINT model performance is 4% better in AUC but 1.7% worse in RMSE. Our experiments on window size suggest that transformer models still struggle with modelling beyond recent performance, and do not yet deliver the step-change observed in NLP. / Transformermodeller, som ger stora förbättringar av AI-textmodeller (NLP), används nu i Knowledge Tracing för att spåra elevernas kunskaper över tid. En av de första, SAINT, visade en hel del förbättring jämfört med de dåvarande SOTA-resultaten på den offentliga EdNet-datauppsättningen och orsakade en ökning av forskning baserad på transformerbaserade modeller. I denna artikeln siktar vi först efter att återskapa SAINT-resultaten på EdNet-datauppsättningen, men vi kan inte rapportera liknande prestanda som den ursprungliga uppsatsen. Detta kan bero på implementeringsdetaljer som vi inte kunde rekonstruera helt. Vi hoppas kunna bana väg för ytterligare reproduktioner, som en allt viktigare del av AI-forskningen. Dessutom tillämpar vi modellen på en företagsdatauppsättning som är mycket större än någon offentlig datauppsättning (fler interaktioner, fler övningar och fler färdigheter). En sådan datauppsättning är å ena sidan mer utmanande (mer blandad kompetens), men å andra sidan ger den mycket mer data (vilket borde hjälpa våra modeller). Vi jämför SAINT-modellen och den framträdande IRT-modellen och finner att SAINT-modellens prestanda är 4% bättre i AUC men 1,7% sämre i RMSE. Våra experiment på fönsterstorlek tyder på att transformermodeller fortfarande kämpar med modellering utöver de senaste prestanda och ännu inte levererar den stegförändring som observerats i NLP.

人類最珍貴的資源，不是石油，而是兒童！尤以我國值此非常時期，兒童更是民族生 機之所繫。如何使他們在安定、和諧的環境下成長，即透過兒童福利的實施，使每一 個兒童都能獲得身心健全的發展，便成為國防建設、經濟發展之外一項重要的課題。 兒童福利是社會福利範疇之一，我國遠在三千多年前即有﹁幼吾幼以及人之幼﹂的兒 童福利思想，歷代對兒童福利措施亦多有明令。惟我國歷代推行有關慈幼的各項工作 ，雖與現代之兒童福利旨趣相符，但其出發點多基於我國固有的仁政思想，措施多隨 為政者之更替而改變，並未有一貫的政策。鑑於兒童地位的重要，與固有傳絲，執政 黨及政府在公布多種社會建設政策後，更於民國六十二年二月八日公布﹁兒童福利法 ﹂，我國兒童福利工作推展於焉有了依據。 我國有關兒童福利的研究，多侷限於社會工作與學理的範圍，甚少以政策的巨視觀點 來探討。故本文以公共政策理論中的系統決策模型和鍾斯（C.O.Jones ）的順序型模 （Sequential Model）為分析架構，期能彌補此一缺失。 全文共分六章，約十萬餘言，茲將各章要點說明如下： 第一章緒論。先說明兒童福利政策的概念，次說明本文的研究動機與目的，限定研究 範圍，最後說明取材方法與所受限制。 第二章問題認定。探討我國兒童福利問題進入議程的過程與途徑。首先說明我國兒童 福利的思想淵源及問題的社會、人口背景；其次自政府、政黨、專家學者等幾方面探 究客觀的事實如何透過主觀的認定而進入議程。 第三章我國兒童福利政策的制定過程。分為政策規劃與合法化過程兩剖分，探討我國 兒童福利法的規劃過程、規劃內容，立法院審議時爭辯的情形與重點。 第四章我國兒童福利政策的執行。分別探討相關法規的擬制、執行機構、經費和人員 四方面，並說明實際執行情形。 第五章我國兒童福利政策的檢討。分別從政策問題的認定、政策制定和政策執行三方 面來檢討。 第六章結論─建議。參酌我國國情及外國法制，並根據前章檢討結果，提出建議，期 能有助於我國兒童福利政策的增進。

Globalization has continuously brought an increased competition among companies, which entails a need for faster and more frequent deliveries of new products. Traditional project management methods, such as stage-gate and waterfall, are commonly used in production development projects and builds on a sequential approach. These methods have proven to have some disadvantages in flexibility, long lead times and it often creates communication barriers between the actors at each stage. The software industry has already encountered these obstacles and responded by introducing agile project management. Which improves the adaptability and allow changes to be made, due to new requirements from stakeholders or customers, throughout the entire development process. However, it remains unknown how agile models can improve production development. The purpose of this study was therefore to investigate how agile models can be applied to production development and what the effects are.  The authors have performed a case study at eight different companies within the automotive industry. The purpose of it has been to gain a deeper understanding about the case companies current production development processes and review how familiar the organizations are with the concept of agile project management. The extraction of the empirical data was conducted by questionnaires, interviews, and document reviews. An analyzation was done by comparing the empirical findings with the theoretical background out of eleven different categories that relates to project management (e.g., project goals, process, customer integration etc.). The analyzation concluded that the case company exclusively conducts their production development project by using a sequential approach.  The analyzation and the eleven categories where, together with the theoretical background about agile project management, later used to create the result by brainstorming different practices to become more agile. The results are presented out of three different scenarios, depending how agile the companies would like to be. For instance, are two process models suggested, one that is completely agile and one that is a hybrid of an agile and a stage-gate. Furthermore, are the implementation of self-organized teams, holistic approach towards internal and external partners, and reduced demand for documentation some of the practices that are suggested. Additionally, are three considerable aspects for the implementation presented.  The expected outcome and effects of applying these practices are discussed in the final chapter. Some of these outcomes are a company culture that will attract and retain talented personnel, where shared responsibilities and authorities triggers the employees to an increased commitment and sense of ownership towards their projects. Furthermore, are the companies expected to experience a more flexible and responsive approach towards conducting production development projects with a high focus on customer requirements and creating customer value.