97 lines
4.8 KiB
HTML
97 lines
4.8 KiB
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<title>Dimitri Lozeve - Skills in Statistics, Data Science and Machine Learning</title>
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<a href="./">Dimitri Lozeve</a>
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<a href="./">Home</a>
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<a href="./projects.html">Projects</a>
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<h1>Skills in Statistics, Data Science and Machine Learning</h1>
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<h1 id="statistics">Statistics</h1>
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<ul>
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<li>Knowledge of Linear Models and Generalised Linear Models (including logistic regression), both in theory and in applications</li>
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<li>Classical Statistical inference (maximum likelihood estimation, method of moments, minimal variance unbiased estimators) and testing (including goodness of fit)</li>
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<li>Nonparametric statistics</li>
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<li>Bootstrap methods, hidden Markov models</li>
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<li>Knowledge of Bayesian Analysis techniques for inference and testing: Markov Chain Monte Carlo, Approximate Bayesian Computation, Reversible Jump MCMC</li>
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<li>Good knowledge of R for statistical modelling and plotting</li>
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</ul>
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<h1 id="data-analysis">Data Analysis</h1>
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<ul>
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<li>Experience with large datasets, for classification and regression</li>
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<li>Descriptive statistics, plotting (with dimensionality reduction)</li>
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<li>Data cleaning and formatting</li>
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<li>Experience with unstructured data coming directly from embedded sensors to a microcontroller</li>
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<li>Experience with large graph and network data</li>
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<li>Experience with live data from APIs</li>
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<li>Data analysis with Pandas, xarray (Python) and the tidyverse (R)</li>
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<li>Basic knowledge of SQL</li>
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</ul>
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<h1 id="graph-and-network-analysis">Graph and Network Analysis</h1>
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<ul>
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<li>Research project on community detection and graph clustering (theory and implementation)</li>
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<li>Research project on Topological Data Analysis for time-dependent networks</li>
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<li>Random graph models</li>
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<li>Estimation in networks (Stein’s method for Normal and Poisson estimation)</li>
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<li>Network Analysis with NetworkX, graph-tool (Python) and igraph (R and Python)</li>
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</ul>
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<h1 id="time-series-analysis">Time Series Analysis</h1>
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<ul>
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<li>experience in analysing inertial sensors data (accelerometer, gyroscope, magnetometer), both in real-time and in post-processing</li>
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<li>use of statistical method for step detection, gait detection, and trajectory reconstruction</li>
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<li>Kalman filtering, Fourier and wavelet analysis</li>
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<li>Machine Learning methods applied to time series (decision trees, SVMs and Recurrent Neural Networks in particular)</li>
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<li>Experience with signal processing functions in Numpy and Scipy (Python)</li>
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</ul>
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<h1 id="machine-learning">Machine Learning</h1>
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<ul>
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<li>Experience in Dimensionality Reduction (PCA, MDS, Kernel PCA, Isomap, spectral clustering)</li>
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<li>Experience with the most common methods and techniques</li>
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<li>Random forests, SVMs, Neural Networks (including CNNs and RNNs), both theoretical knowledge and practical experience</li>
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<li>Bagging and boosting estimators</li>
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<li>Cross-validation</li>
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<li>Kernel methods, reproducing kernel Hilbert spaces, collaborative filtering, variational Bayes, Gaussian processes</li>
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<li>Machine Learning libraries: Scikit-Learn, PyTorch, TensorFlow, Keras</li>
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</ul>
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<h1 id="simulation">Simulation</h1>
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<ul>
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<li>Inversion, Transformation, Rejection, and Importance sampling</li>
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<li>Gibbs sampling</li>
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<li>Metropolis-Hastings</li>
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<li>Reversible jump MCMC</li>
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<li>Hidden Markov Models and Sequential Monte Carlo Methods</li>
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</ul>
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