78 lines
2.8 KiB
Org Mode
78 lines
2.8 KiB
Org Mode
---
|
|
title: "Skills in Statistics, Data Science and Machine Learning"
|
|
date: 2018-06-30
|
|
---
|
|
|
|
|
|
* Statistics
|
|
|
|
- Knowledge of Linear Models and Generalised Linear Models
|
|
(including logistic regression), both in theory and in
|
|
applications
|
|
- Classical Statistical inference (maximum likelihood estimation,
|
|
method of moments, minimal variance unbiased estimators) and
|
|
testing (including goodness of fit)
|
|
- Nonparametric statistics
|
|
- Bootstrap methods, hidden Markov models
|
|
- Knowledge of Bayesian Analysis techniques for inference and
|
|
testing: Markov Chain Monte Carlo, Approximate Bayesian
|
|
Computation, Reversible Jump MCMC
|
|
- Good knowledge of R for statistical modelling and plotting
|
|
|
|
* Data Analysis
|
|
|
|
- Experience with large datasets, for classification and regression
|
|
- Descriptive statistics, plotting (with dimensionality reduction)
|
|
- Data cleaning and formatting
|
|
- Experience with unstructured data coming directly from embedded
|
|
sensors to a microcontroller
|
|
- Experience with large graph and network data
|
|
- Experience with live data from APIs
|
|
- Data analysis with Pandas, xarray (Python) and the tidyverse (R)
|
|
- Basic knowledge of SQL
|
|
|
|
* Graph and Network Analysis
|
|
|
|
- Research project on community detection and graph clustering
|
|
(theory and implementation)
|
|
- Research project on Topological Data Analysis for time-dependent
|
|
networks
|
|
- Random graph models
|
|
- Estimation in networks (Stein's method for Normal and Poisson
|
|
estimation)
|
|
- Network Analysis with NetworkX, graph-tool (Python) and igraph (R
|
|
and Python)
|
|
|
|
* Time Series Analysis
|
|
|
|
- experience in analysing inertial sensors data (accelerometer,
|
|
gyroscope, magnetometer), both in real-time and in post-processing
|
|
- use of statistical method for step detection, gait detection, and
|
|
trajectory reconstruction
|
|
- Kalman filtering, Fourier and wavelet analysis
|
|
- Machine Learning methods applied to time series (decision trees,
|
|
SVMs and Recurrent Neural Networks in particular)
|
|
- Experience with signal processing functions in Numpy and Scipy
|
|
(Python)
|
|
|
|
* Machine Learning
|
|
|
|
- Experience in Dimensionality Reduction (PCA, MDS, Kernel PCA,
|
|
Isomap, spectral clustering)
|
|
- Experience with the most common methods and techniques
|
|
- Random forests, SVMs, Neural Networks (including CNNs and RNNs),
|
|
both theoretical knowledge and practical experience
|
|
- Bagging and boosting estimators
|
|
- Cross-validation
|
|
- Kernel methods, reproducing kernel Hilbert spaces, collaborative
|
|
filtering, variational Bayes, Gaussian processes
|
|
- Machine Learning libraries: Scikit-Learn, PyTorch, TensorFlow,
|
|
Keras
|
|
|
|
* Simulation
|
|
|
|
- Inversion, Transformation, Rejection, and Importance sampling
|
|
- Gibbs sampling
|
|
- Metropolis-Hastings
|
|
- Reversible jump MCMC
|
|
- Hidden Markov Models and Sequential Monte Carlo Methods
|