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Demote headers to avoid first-level as <h1>

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Dimitri Lozeve 2020-05-26 17:21:53 +02:00
parent aa841f4ba2
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_site/skills.html
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@ -44,7 +44,7 @@
</article>
<h1 id="statistics">Statistics</h1>
<h2 id="statistics">Statistics</h2>
<ul>
<li>Knowledge of Linear Models and Generalised Linear Models (including logistic regression), both in theory and in applications</li>
<li>Classical Statistical inference (maximum likelihood estimation, method of moments, minimal variance unbiased estimators) and testing (including goodness of fit)</li>
@ -53,7 +53,7 @@
<li>Knowledge of Bayesian Analysis techniques for inference and testing: Markov Chain Monte Carlo, Approximate Bayesian Computation, Reversible Jump MCMC</li>
<li>Good knowledge of R for statistical modelling and plotting</li>
</ul>
<h1 id="data-analysis">Data Analysis</h1>
<h2 id="data-analysis">Data Analysis</h2>
<ul>
<li>Experience with large datasets, for classification and regression</li>
<li>Descriptive statistics, plotting (with dimensionality reduction)</li>
@ -64,7 +64,7 @@
<li>Data analysis with Pandas, xarray (Python) and the tidyverse (R)</li>
<li>Basic knowledge of SQL</li>
</ul>
<h1 id="graph-and-network-analysis">Graph and Network Analysis</h1>
<h2 id="graph-and-network-analysis">Graph and Network Analysis</h2>
<ul>
<li>Research project on community detection and graph clustering (theory and implementation)</li>
<li>Research project on Topological Data Analysis for time-dependent networks</li>
@ -72,7 +72,7 @@
<li>Estimation in networks (Stein’s method for Normal and Poisson estimation)</li>
<li>Network Analysis with NetworkX, graph-tool (Python) and igraph (R and Python)</li>
</ul>
<h1 id="time-series-analysis">Time Series Analysis</h1>
<h2 id="time-series-analysis">Time Series Analysis</h2>
<ul>
<li>experience in analysing inertial sensors data (accelerometer, gyroscope, magnetometer), both in real-time and in post-processing</li>
<li>use of statistical method for step detection, gait detection, and trajectory reconstruction</li>
@ -80,7 +80,7 @@
<li>Machine Learning methods applied to time series (decision trees, SVMs and Recurrent Neural Networks in particular)</li>
<li>Experience with signal processing functions in Numpy and Scipy (Python)</li>
</ul>
<h1 id="machine-learning">Machine Learning</h1>
<h2 id="machine-learning">Machine Learning</h2>
<ul>
<li>Experience in Dimensionality Reduction (PCA, MDS, Kernel PCA, Isomap, spectral clustering)</li>
<li>Experience with the most common methods and techniques</li>
@ -90,7 +90,7 @@
<li>Kernel methods, reproducing kernel Hilbert spaces, collaborative filtering, variational Bayes, Gaussian processes</li>
<li>Machine Learning libraries: Scikit-Learn, PyTorch, TensorFlow, Keras</li>
</ul>
<h1 id="simulation">Simulation</h1>
<h2 id="simulation">Simulation</h2>
<ul>
<li>Inversion, Transformation, Rejection, and Importance sampling</li>
<li>Gibbs sampling</li>