Increasingly flexible and efficient industrial processes and automation systems are developed by integrating computational systems and physical processes, thereby forming large heterogeneous systems of cyber-physical systems. Such systems depend on particular data models and payload formats for communication, and making different entities interoperable is a challenging problem that drives the engineering costs and time to deployment. Interoperability is typically established and maintained manually using domain knowledge and tools for processing and visualization of symbolic metadata, which limits the scalability of the present approach. The vision of next generation automation frameworks, like the Arrowhead Framework, is to provide autonomous interoperability solutions. In this thesis the problem to automatically establish interoperability between cyber-physical systems is reviewed and formulated as a mathematical optimisation problem, where symbolic metadata and message payloads are combined with machine learning methods to enable message translation and improve system of systems utility. An autoencoder based implementation of the model is investigated and simulation results for a heating and ventilation system are presented, where messages are partially translated correctly by semantic interpolation and generalisation of the latent representations. A maximum translation accuracy of 49% is obtained using this unsupervised learning approach. Further work is required to improve the translation accuracy, in particular by further exploiting metadata in the model architecture and autoencoder training protocol, and by considering more advanced regularization methods and utility optimization. / Productive 4.0

Chronic pain (CP) is a personal and economic burden that affects more than 30% of the world's population. While being the leading cause of disability, it is complicated to diagnose and manage. The optimal way to treat CP is to identify the pain mechanism or the underlying cause. The substantial overlap of the pain mechanisms (i.e., Nociceptive, Neuropathic, and Nociplastic) usually makes identification unreachable in a clinical setting where finding the dominant mechanism is complicated. Additionally, many specialists regard CP classification as a spectrum or continuum. Despite the importance, a data-driven way to identify co-existing CP mechanisms and quantification is still absent. This work successfully identified the co-existing CP mechanisms within a patient using Unsupervised Learning while quantifying them without the help of diagnosis established by the clinicians. Two different datasets from different cohorts comprised of patient-reported history and questionnaires were used in this work. Unsupervised Learning (k-prototypes) revealed notable overlaps in the data. It was further emphasized by the outcomes of the Semi-supervised Learning algorithms when the same trend was observed with some diagnosis or class information. It became evident that the CP mechanisms overlap and cannot be classified as distinct conditions. Additionally, mixed pain mechanisms do not make an individual cluster or class, and CP should be considered as a continuum. To reduce data dimension and extract hidden features, Autoencoder was used. Using an overlapping clustering technique, the pain mechanisms were identified. The pain mechanisms were also quantified while elucidating overlaps, and the dominant CP mechanism was successfully pointed out with explainable element. The hamming loss of 0.43 and average precision of 0.5 were achieved when considered as a multi-label classification problem. This work is a data-driven validation that there are significant overlaps in CP conditions, and CP should be considered a continuum where all CP mechanisms may co-exist. / Thesis / Master of Applied Science (MASc) / Chronic pain (CP) is a global burden and the primary cause for patients to seek medical attention. Despite continuous efforts in this area, CP remains clinically challenging to manage. The most effective method of treating CP is identifying the underlying cause or mechanism, which is often unattainable. This thesis attempted to identify the CP mechanisms existing in a patient while quantifying them from patient-reported history and questionnaire data. Unsupervised Learning was used to identify clinically meaningful clusters that revealed the three main CP mechanisms, i.e., Nociceptive, Neuropathic, and Nociplastic, achieving acceptable hamming loss (0.43) and average precision (0.5). The results exhibited that the CP mechanisms co-exist and CP should be regarded as a continuum rather than distinct entities. The algorithm successfully indicated the dominant CP mechanism, a goal for optimal CP management and treatment. The results were also validated by a comparative analysis with data from another cohort that demonstrated a similar trend.

Here, we show that training with multiple noncommunicable diseases (NCDs) is both feasible and beneficial to modeling this class of diseases. We first use data from the Cancer Genome Atlas (TCGA) to train a pan cancer model, and then characterize the information the model has learned about the cancers. In doing this we show that the model has learned concepts that are relevant to the task of cancer classification. We also test the model on datasets derived independently of the TCGA cohort and show that the model is robust to data outside of its training distribution such as precancerous legions and metastatic samples. We then utilize the cancer model as the basis of a transfer learning study where we retrain it on other, non-cancer NCDs. In doing so we show that NCDs with very differing underlying biology contain extractible information relevant to each other allowing for a broader model of NCDs to be developed with existing datasets. We then test the importance of the samples source tissue in the model and find that the NCD class and tissue source may not be independent in our model. To address this, we use the tissue encodings to create augmented samples. We test how successfully we can use these augmented samples to remove or diminish tissue source importance to NCD class through retraining the model. In doing this we make key observations about the nature of concept importance and its usefulness in future neural network explainability efforts.

Phase transitions are fundamental phenomena in (quantum) many-body systems. They are associated with changes in the macroscopic physical properties of the system in response to the alteration in the conditions controlled by one or more parameters, like temperature or coupling constants. Quantum phase transitions are particularly intriguing as they reveal new insights into the fundamental nature of matter and the laws of physics. The study of phase transitions in such systems is crucial in aiding our understanding of how materials behave in extreme conditions, which are difficult to replicate in laboratory, and also the behavior of exotic states of matter with unique and potentially useful properties like superconductors and superfluids. Moreover, this understanding has other practical applications and can lead to the development of new materials with specific properties or more efficient technologies, such as quantum computers. Hence, detecting the transition point from one phase of matter to another and constructing the corresponding phase diagram is of great importance for examining many-body systems and predicting their response to external perturbations. Traditionally, phase transitions have been identified either through analytical methods like mean field theory or numerical simulations. The pinpointing of the critical value normally involves the measure of specific quantities such as local observables, correlation functions, energy gaps, etc. reflecting the changes in the physics through the transition. However, the latter approach requires prior knowledge of the system to calculate the order parameter of the transition, which is uniquely associated to its universality class. Recently, another method has gained more and more attention in the physics community. By using raw and very general representative data of the system, one can resort to machine learning techniques to distinguish among patterns within the data belonging to different phases. The relevance of these techniques is rooted in the ability of a properly trained machine to efficiently process complex data for the sake of pursuing classification tasks, pattern recognition, generating brand new data and even developing decision processes. The aim of this thesis is to explore phase transitions from this new and promising data-centric perspective. On the one hand, our work is focused on the developement of new machine learning architectures using state-of-the-art and interpretable models. On the other hand, we are interested in the study of the various possible data which can be fed to the artificial intelligence model for the mapping of a quantum many-body system phase diagram. Our analysis is supported by numerical examples obtained via matrix-product-states (MPS) simulations for several one-dimensional zero-temperature systems on a lattice such as the XXZ model, the Extended Bose-Hubbard model (EBH) and the two-species Bose Hubbard model (BH2S). In Part I, we provide a general introduction to the background concepts for the understanding of the physics and the numerical methods used for the simulations and the analysis with deep learning. In Part II, we first present the models of the quantum many-body systems that we study. Then, we discuss the machine learning protocol to identify phase transitions, namely anomaly detection technique, that involves the training of a model on a dataset of normal behavior and use it to recognize deviations from this behavior on test data. The latter can be applied for our purpose by training in a known phase so that, at test-time, all the other phases of the system are marked as anomalies. Our method is based on Generative Adversarial Networks (GANs) and improves the networks adopted by the previous works in the literature for the anomaly detection scheme taking advantage of the adversarial training procedure. Specifically, we train the GAN on a dataset composed of bipartite entanglement spectra (ES) obtained from Tensor Network simulations for the three aforementioned quantum systems. We focus our study on the detection of the elusive Berezinskii-Kosterlitz-Thouless (BKT) transition that have been object of intense theoretical and experimental studies since its first prediction for the classical two-dimensional XY model. The absence of an explicit symmetry breaking and its gappless-to-gapped nature which characterize such a transition make the latter very subtle to be detected, hence providing a challenging testing ground for the machine-driven method. We train the GAN architecture on the ES data in the gapless side of BKT transition and we show that the GAN is able to automatically distinguish between data from the same phase and beyond the BKT. The protocol that we develop is not supposed to become a substitute to the traditional methods for the phase transitions detection but allows to obtain a qualitative map of a phase diagram with almost no prior knowledge about the nature and the arrangement of the phases -- in this sense we refer to it as agnostic -- in an automatic fashion. Furthermore, it is very general and it can be applied in principle to all kind of representative data of the system coming both from experiments and numerics, as long as they have different patterns (even hidden to the eye) in different phases. Since the kind of data is crucially linked with the success of the detection, together with the ES we investigate another candidate: the probability density function (PDF) of a globally U(1) conserved charge in an extensive sub-portion of the system. The full PDF is one of the possible reductions of the ES which is known to exhibit relations and degeneracies reflecting very peculiar aspects of the physics and the symmetries of the system. Its patterns are often used to tell different kinds of phases apart and embed information about non-local quantum correlations. However, the PDF is measurable, e.g. in quantum gas microscopes experiments, and it is quite general so that it can be considered not only in the cases of the study but also in other systems with different symmetries and dimensionalities. Both the ES and the PDF can be extracted from the simulation of the ground state by dividing the one-dimensional chain into two complementary subportions. For the EBH we calculate the PDF of the bosonic occupation number in a wide range of values of the couplings and we are able to reproduce the very rich phase diagram containing several phases (superfluid, Mott insulator, charge density wave, phase separation of supersolid and superfluid and the topological Haldane insulator) just with an educated gaussian fit of the PDF. Even without resorting to machine learning, this analysis is instrumental to show the importance of the experimentally accessible PDF for the task. Moreover, we highlight some of its properties according to the gapless and gapped nature of the ground state which require a further investigation and extension beyond zero-temperature regimes and one-dimensional systems. The last chapter of the results contains the description of another architecture, namely the Concrete Autoencoder (CAE) which can be used for detecting phase transitions with the anomaly detection scheme while being able to automatically learn what the most relevant components of the input data are. We show that the CAE can recognize the important eigenvalues out of the entire ES for the EBH model in order to characterize the gapless phase. Therefore the latter architecture can be used to provide not only a more compact version of the input data (dimensionality reduction) -- which can improve the training -- but also some meaningful insights in the spirit of machine learning interpretability. In conclusion, in this thesis we describe two advances in the solution to the problem of phase recognition in quantum many-body systems. On one side, we improve the literature standard anomaly detection protocol for an automatic and agnostic identification of the phases by employing the GAN network. Moreover, we implement and test an explainable model which can make the interpretation of the results easier. On the other side we put the focus on the PDF as a new candidate quantity for the scope of discerning phases of matter. We show that it contains a lot of information about the many-body state being very general and experimentally accessible.

Le processus du don d’organes, crucial pour la survie de nombreux patients, ne répond pas à la demande croissante. Il dépend d’une identification, par les cliniciens, des potentiels donneurs d’organes. Cette étape est imparfaite et manque entre 30% et 60% des potentiels donneurs d’organes et ce indépendamment des pays étudiés. Améliorer ce processus est un impératif à la fois moral et économique. L’objectif de ce mémoire était de développer et valider un modèle afin de détecter automatiquement les potentiels donneurs d’organes. Pour ce faire, les données cliniques de l’ensemble des patients adultes hospitalisés aux soins intensifs du CHUM entre 2012 et 2019 ont été utilisées. 103 valeurs de laboratoires temporelles différentes et 2 valeurs statiques ont été utilisées pour développer un modèle de réseaux de neurones convolutifs entrainé à prédire les potentiels donneurs d’organes. Ce modèle a été comparé à un modèle fréquentiste linéaire non temporel. Le modèle a par la suite été validé dans une population externe cliniquement distincte. Différentes stratégies ont été comparées pour peaufiner le modèle dans cette population externe et améliorer les performances. Un total de 19 463 patients, dont 397 donneurs potentiels, ont été utilisés pour développer le modèle et 4 669, dont 36 donneurs potentiels, ont été utilisés pour la validation externe. Le modèle démontrait une aire sous la courbe ROC (AUROC) de 0.966 (IC95% 0.9490.981), supérieure au modèle fréquentiste linéaire (AUROC de 0.940 IC95% 0.908-0.969, p=0.014). Le modèle était aussi supérieur dans certaines sous populations d’intérêt clinique. Dans le groupe de validation externe, l’AUROC du modèle de réseaux de neurones était de 0.820 (0.682-0.948) augmentant à 0.874 (0.731-0.974) à l’aide d’un ré-entrainement. Ce modèle prometteur a le potentiel de modifier et d’améliorer la détection des potentiels donneurs d’organes. D’autres étapes de validation prospectives et d’amélioration du modèle, notamment l’ajout de données spécifiques, sont nécessaires avant une utilisation clinique de routine. / The organ donation process, however crucial for many patients’ survival, is not enough to address the increasing demand. Its efficiency depends on potential organ donors’ identification by clinicians. This imperfect step misses between 30%–60% of potential organ donor. Improving that process is a moral and economic imperative. The main goal of this work was to address that liming step by developing and validating a predictive model that could automatically detect potential organ donors. The clinical data from all patients hospitalized, between 2012 and 2019 to the CHUM critical care units were extracted. The temporal evolution of 103 types of laboratory analysis and 2 static clinical data was used to develop and test a convolutive neural network (CNN), trained to predict potential organ donors. This model was compared to a non-temporal logistical model as a baseline. The CNN model was validated in a clinically distinct external population. To improve the performance in this external cohort, strategies to fine-tune the network were compared. 19 463 patients, including 397 potential organ donors, were used to create the model and 4 669 patients, including 36 potential organ donors, served as the external validation cohort. The CNN model performed better with an AUROC of 0.966 (IC95% 0.949-0.981), compared to the logistical model (AUROC de 0.940 IC95% 0.908-0.969, p=0.014). The CNN model was also superior in specific subpopulation of increased clinical interest. In the external validation cohort, the CNN model’s AUROC was 0.820 (0.682-0.948) and could be improved to 0.874 (0.731-0.974) after fine tuning. This promising model could change potential organ donors' detection for the better. More studies are however required to improve the model, by adding more types of data, and to validate prospectively the mode before routine clinical usage.

Parkinson’s disease is a brain disorder associated with reduced dopamine levels in the brain, affecting cognition and motor control in the human brain. One of the motor controls that can be affected is eye movements and can therefore be critically affected in patients with Parkinson’s disease. Eye movement can be measured using eye trackers, and this data can be used for analyzing the eye movement characteristics in Parkinson’s disease. The eye movement analysis provides the possibility of diagnostics and can therefore lead to further insights into Parkinson’s disease. In this thesis, feature extraction of clinical relevance in diagnosing Parkinson’s patients from eye movement data is studied. We have used an autoencoder (AE) constructed to learn micro and macro-scaled representation for eye movements and constructed three different models. Learning of the AEs was evaluated using the F1 score, and differences were statistically assessed using the Wilcoxon sign rank test. Extracted features from data based on patients and healthy subjects were visualized using t-SNE. Using the extracted features, we have measured differences in features using cosine and Mahalanobis distances. We have furthermore clustered the features using fuzzy c-means. Qualities of the generated clusters were assessed by F1-score, fuzzy partition coefficient, Dunn’s index and silhouette index. Based on successful tests using a test data set of a previous publication, we believe that the network used in this thesis has learned to represent natural eye movement from subjects allowed to move their eye freely. However, distances, visualizations, clustering all suggest that latent representations from the autoencoder do not provide a good separation of data from patients and healthy subjects. We, therefore, conclude that a micro-macro autoencoder does not suit the purpose of generating a latent representation of saccade movements of the type used in this thesis. / Parkinsons sjukdom är en hjärnsjukdom orsakad av minskade dopaminnivåer i hjärnan, vilket påverkar kognition och motorisk kontroll i människans hjärna. En av de motoriska kontrollerna som kan påverkas är ögonrörelser och kan därför vara kritiskt påverkat hos patienter diagnostiserade med Parkinsons sjukdom. Ögonrörelser kan mätas med hjälp av ögonspårare, som i sin tur kan användas för att analysera ögonrörelsens egenskaper vid Parkinsons sjukdom. Ögonrörelseanalysen ger möjlighet till diagnostik och kan därför leda till ytterligare förståelse för Parkinsons sjukdom. I denna avhandling studeras särdragsextraktion av ögonrörelsedata med en klinisk relevans vid diagnos av Parkinsonpatienter. Vi har använt en autoencoder (AE) konstruerad för att lära sig mikro- och makrosackadrepresentation för ögonrörelser och konstruerat tre olika modeller. Inlärning av AE utvärderades med hjälp av F1-poängen och skillnader bedömdes statistiskt med hjälp av Wilcoxon rank test. Särdragsextraktionen visualiserades med t-SNE och med hjälp av resultatet ifrån särdragsextraktion har vi mätt skillnader med cosinus- och Mahalanobis- avstånd. Vi har dessutom grupperat resultatet ifrån särdragsextraktionen med fuzzy c-means. Kvaliteten hos de genererade klusterna bedömdes med F1- poäng, suddig fördelningskoefficient, Dunns index och silhuettindex.Sammanfattningsvis finner vi att en mikro-makro-autokodare inte passar syftet med att analysera konstgjorda ögonrörelsesdata. Vi tror att nätverket som används i denna avhandling har lärt sig att representera naturlig ögonrörelse ifrån en person som fritt får röra sina ögon.

Deep learning-based dose prediction is a promising approach to automated radiotherapy planning but carries with it the risk of failing silently when the inputs are highly abnormal compared to the training data. One way to address this issue is to develop a dedicated outlier detector capable of detecting anomalous patient geometries. I examine the potential of so-called generative models to handle this task. These models are promising due to being able to model the distribution of the input data regardless of the downstream task, but they have also been shown to suffer from serious biases when applied to outlier detection. No consensus has been reached regarding the root cause of these biases, or how to address them. I investigate this by attempting to design a variational autoencoder-based outlier detector trained to detect anomalous samples of shapes represented in a binary voxel format. I find the standard procedure application to suffer from severe bias when encountering cropped shapes, leading to systematic misclassification of some outlier patient cases. I overcome this by adopting a segmentation metric as an out-of-distribution metric and show that this outperforms recently proposed general-purpose solutions to the likelihood bias issue. I then benchmark my proposed method on clinical samples and conclude that this approach achieves performance comparable to a one-class support vector machine model that uses handcrafted domain-specific features. / Djupinlärningsbaserad dosprediktion är en mycket lovande metod för att automatiskt generera behandlingsplaner för strålterapi. Djupinlärningsmodeller kan dock endast förväntas fungera på data som är tillräckligt lik träningsdatan, vilket skapar en säkerhetsrisk i kliniska miljöer. Ett möjlig lösning på detta problem är att använda en särskild detektor som klarar av att identifiera avvikande data. I denna uppsats undersöker jag om en generativa djupinlärningsmodell kan användas som en sådan detektor. Generativa modeller är särskilt intressanta för detta ändamål då de är både kraftfulla och flexibla. Dessvärre har generativa modeller visats kunna vilseledas av vissa typer av data. Orsakerna och de underliggande faktorerna till detta har ännu inte identifierats. Jag undersöker denna problematik genom att designa en detektor baserad på en variationell autokodare. Jag upptäcker att den en naiv applikation av denna modell inte är tillräcklig för den kliniska datan, då modellen systematiskt felvärderar beskärda former. Jag löser detta problem genom att nyttja ett modifierat segmenteringsmått som detektionsmått, och visar att denna metod fungerar bättre än mer allmänna lösningar på vilseledningsproblemet. Jag evaluerar metoderna på klinisk data och finner att min metod fungerar lika bra som en en-klass stödvektormaskin som använder sig av handgjorda domänspecifika features.

The strive for cost reduction of services and repairs combined with a desire for increased vehicle reliability has led to the development of predictive maintenance programs. In maintenance plans, accurate forecasts and predictions regarding which components in a vehicle is in risk of a breakdown is bene_cial to obtain since this enables components to be predictively exchanged or serviced before they break down and cause unnecessary downtime. Previous works in data driven predictive maintenance models typically utilize customer and operational data to predict component wear trough regressive or classi_er models. In this thesis the possibilities and bene_ts associated with utilizing vehicle repair and service history data for trucks in a predictive model is investigated. The repair and service data is a time series of irregularly sampled visits to a service centre and is used in conjunction with operational data and chassis con_guration data collected by a truck manufacturer. To tackle the problem a Random Forest, a Neural Network as well as a Recurrent Neural Network model was tested on the various datasets. The Recurrent Neural Network model made it possible to utilize the entire vehicle repair time series data whereas the Random Forest model used a condensed form of the repair data. The Recurrent model proved to perform signi_cantly better than the Neural Network model trained on operational data however it was not proven signi_cantly better than a Random Forest model trained on the condensed form of repair data. A conclusion that can be drawn is that repair history data can increase the performance of a predictive model, however it is unclear if the time sequence plays a part or if a list of previously exchanged parts works equally well. / Strävan efter att reducera kostnader av reparationer och service samt att öka fordons pålitlighet har lett till utvecklingen av prediktiva underhållsprogram. Träffsäkra förutsägeleser och prediktioner kring vilka delar som riskerar att fallera möjliggör prediktiva utbytelser eller service av delar innan de går sönder. Tidigare arbeten i prediktivt underhåll använder sig vanligen av kunddata och operationell data för att generera en prediktion genom regressions eller klassificeringsmetoder. I det här examensarbetet utforskas möjligheterna och fördelarna med att använda verkstadsdata från lastbilar i en prediktiv modell. Verkstadsdatan består av en oregelbundet genererad tidsserie av besök till en serviceanläggning och används i kombination med operationell data samt chassiutförandedata. För att angripa problemet användes en Random Forest, en Neuronnäts samt en Recurrent (Återkommande) Neuronnätsmodell på de olika datakällorna. Recurrent Neuronnätsmodellen möjliggjorde användandet av kompletta tidserieverkstadsdatan och denna modell visade sig ge bäst resultat men kunde inte påvisas  vara signifikant bättre än en Random Forest modell som tränades på en komprimerad variant av verkstadsdatan.  En slutsats som kan dras av arbetet är att verkstadsdatan kan öka prestandan i en prediktiv model men att det är oklart om det är tidssekvensen av datat som ger ökningen eller om det fungerar lika bra med en lista över tidigare utbytta delar.

Drinking water is one of our most important re- sources, so the ability to reliably monitor harmful contaminations in our water distribution network is vital. In order to minimize false alarms for water monitoring, while keeping a high sensitivity, a machine learning approach was evaluated in this project. Measurement data captured with a new kind of sensor, an electronic tongue, was provided by Linköping university. The solution was an artificial neural network, in the structure of an Autoencoder, which could learn the dynamic behaviour of natural deviations and with a false alarm rate of approximately one false alarm per week. This was done by evaluating the data and assembling an input structure to account for daily cyclic phenomena, which then was used to train the neural network. The solution could detect anomalies as small as 1.5% by comparing the input with the reconstructed vector, and raise an alarm. In conclusion, an Autoencoder is a viable method for detecting anomalies in water quality. / Drickvatten är en av våra mest värdefulla tillgångar, det är därför mycket viktigt att det finns sätt att pålitligt övervaka om dricksvattennätet blivit förorenat. För att kunna minimera antalet falsklarm och samtidigt ha hög känslighet mot dessa föroreningar undersöktes och implementerades en lösning med maskininlärningsalgoritmer. Mätdata tillhandahölls av Linköpings universitet och kom från en ny sensor kallad elektronisk tunga. Lösningen var ett artificiellt neuralt nätverk i form av en Autoencoder, som kunde lära sig det dynamiska beteende som ofarliga avvikelser utgjorde. Detta gav en lösning som i medel gav ett falsklarm per sju dagar. Detta gjordes genom att utvärdera rådata och konstruera en struktur på indata som tar hänsyn till dygnsbunda naturliga fenomen. Denna struktur användes sedan för att träna det neurala nätverket. Lösningen kunde upptäcka fel ner till 1.5% genom att jämföra indata med den rekonstruerade vektorn, och på så sätt ge ett alarm. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

This dissertation addresses multi-layer optimization aspects of multiple input multiple output (MIMO) and deep learning-based communication systems. The initial focus is on the rate optimization for multi-user MIMO (MU-MIMO) configurations; specifically, multiple access channel (MAC) and interference channel (IC). First, the ergodic sum rates of MIMO MAC and IC configurations are determined by jointly integrating the error and overhead effects due to channel estimation (training) and feedback into the rate optimization. Then, we investigated methods that will increase the achievable rate for parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We derive a generalized iterative waterfilling algorithm for power allocation that maximizes the ergodic achievable rate. We verified the sum rate improvement with our proposed scheme through extensive simulation tests. Next, we introduce a novel physical layer scheme for single user MIMO spatial multiplexing systems based on unsupervised deep learning using an autoencoder. Both transmitter and receiver are designed as feedforward neural networks (FNN) and constellation diagrams are optimized to minimize the symbol error rate (SER) based on the channel characteristics. We first evaluate the SER in the presence of a constant Rayleigh-fading channel as a performance upper bound. Then, we quantize the Gaussian distribution and train the autoencoder with multiple quantized channel matrices. The channel is provided as an input to both the transmitter and the receiver. The performance exceeds that of conventional communication systems both when the autoencoder is trained and tested with single and multiple channels and the performance gain is sustained after accounting for the channel estimation error. Moreover, we evaluate the performance with increasing number of quantization points and when there is a difference between training and test channels. We show that the performance loss is minimal when training is performed with sufficiently large number of quantization points and number of channels. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput. / Doctor of Philosophy / Multiple Input Multiple Output (MIMO) wireless systems include multiple antennas both at the transmitter and receiver and they are widely used today in cellular and wireless local area network systems to increase robustness, reliability and data rate. Multi-user MIMO (MU-MIMO) configurations include multiple access channel (MAC) where multiple transmitters communicate simultaneously with a single receiver; interference channel (IC) where multiple transmitters communicate simultaneously with their intended receivers; and broadcast channel (BC) where a single transmitter communicates simultaneously with multiple receivers. Channel state information (CSI) is required at the transmitter to precode the signal and mitigate interference effects. This requires CSI to be estimated at the receiver and transmitted back to the transmitter in a feedback loop. Errors occur during both channel estimation and feedback processes. We initially analyze the achievable rate of MAC and IC configurations when both channel estimation and feedback errors are taken into account in the capacity formulations. We treat the errors associated with channel estimation and feedback as additional noise. Next, we develop methods to maximize the achievable rate for IC by using interference cancellation techniques at the receivers when the interference is very strong. We consider parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We develop a power allocation scheme which maximizes the ergodic achievable rate of the communication systems. We verify the performance improvement with our proposed scheme through simulation tests. Standard optimization techniques are used to determine the fundamental limits of MIMO communications systems. However, there is still a gap between current operational systems and these limits due to complexity of these solutions and limitations in their assumptions. Next, we introduce a novel physical layer scheme for MIMO systems based on machine learning; specifically, unsupervised deep learning using an autoencoder. An autoencoder consists of an encoder and a decoder that compresses and decompresses data, respectively. We designed both the encoder and the decoder as feedforward neural networks (FNNs). In our case, encoder performs transmitter functionalities such as modulation and error correction coding and decoder performs receiver functionalities such as demodulation and decoding as part of the communication system. Channel is included as an additional layer between the encoder and decoder. By incorporating the channel effects in the design process of the autoencoder and jointly optimizing the transmitter and receiver, we demonstrate the performance gains over conventional MIMO communication schemes. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput.