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56 statistics teachers in France

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56 statistics teachers in France

Mattia - Paris69€
69€Econometrics & Statistics (R, SPSS, Eviews, Gretl, Stata) for master thesis and assignments
Trusted teacher: MsC in Engineering with top marks and research assistant of Econometrics for Italian top University. Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading. Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl Technical Skills (application and often implementation from scratch): 1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity 2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution 3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment 4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup 5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
Statistics · Economics for adults · Computer programming
Dave - Paris46€
46€Machine learning course / project by Doctor in machine learning and actuarial science
Trusted teacher: Hello, I am an experienced machine learning teacher with 5 years of expertise in teaching this discipline at all levels. My expertise using Python and R allows me to teach different machine learning algorithms such as neural networks, decision trees and clustering algorithms. I am also experienced in using popular Python and R libraries such as TensorFlow, Keras, Scikit-learn and ggplot2. In addition to my machine learning skills, I am able to help students read and understand research papers for their presentations, as well as work on projects in Python and R. My commitment to machine learning is passionate and I enjoy sharing my knowledge with my students. If you are interested in my services as a machine learning teacher for all levels, do not hesitate to contact me. In addition to my machine learning skills, I am also able to help you with mathematics, statistics and dissertation writing. I am available to teach the following subjects: 1.Python or R 2. Data exploration 3.Machine learning 3.1. Intro ML 3.2. Linear Model -> Linear Models for Regression and Classification 3.3. kernel -> Kernelization 3.4. Model selection 3.5. model set, -> Bagging / RandomForest, Boosting (XGBoost, LightGBM,...) , Stacking 3.6. Data preprocessing -> Data pre-processing -> Pipelines: choose the right preprocessing steps and models in your pipeline -> Cross validation 3.7. Neural Networks -> Neural architectures -> Training neural nets: Forward pass: Tensor operations and Backward pass: Backpropagation -> Neural network design: Activation functions, weight initialization and Optimizers -> Neural networks in practice: Model selection, Early stopping, Memorization capacity and information bottleneck, L1/L2 regularization, Dropout, Batch normalization 3.8. Convolutional Neural Networks -> Convolved Image -> Convolutional neural networks ->Data increase -> Model interpretation -> Using pre-trained networks (transfer learning) 3.9. Neural Networks for text -> Bag of word representations, Word embeddings, Word2Vec, FastText, GloVe
Math · Statistics · Computer science
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Matisse - Clermont-Ferrand35€
35€Associate Professor Statistics 15 Years of Experience Quality Courses
Trusted teacher: Hello Miss, Sir , I am the professor of one of the best establishments of Clermont Ferrand. For 15 years I realize upgrades in statistics with a very high rate of success. I intervene in favor of students in difficulty who want to fill important gaps but also for others who aim to be at the top of the class. My individualized pedagogy allows me to understand, adapt and achieve the specific goal of each student. The statistics courses are very focused on methodology and pedagogy because these are the keys to successful schooling. The theoretical concepts studied in class such as the probability laws, the random variables, the expectations, the variances, the degrees of confidence or the standard deviations are explained again in a clear and very detailed way so that the student reaches a better comprehension. The practice of the course is also very qualitative. The student is guided step by step during the exercises concerning the laws of probability, the random variables, the expectations, the variances, the degrees of confidence or even the standard deviations then during the corrections in the aim that it reduces the most possible mistakes. A method of working in high-performance statistics and organizational advice specific to the subject are also inculcated. Throughout my 15 years of experience, I intervened in favor of about 300 students (high school students / adults attending an extracurricular course) which allows me to master all the programs on the fingertips statistics in the main branches such as probability laws, random variables, expectations, variances, degrees of confidence or standard deviations. I managed to increase my students by an average of 4 points each quarter to reach in the best cases 19/20 at the end of the year. I have an aggregation, the highest diploma to teach courses, which is a guarantee of my aptitude for teaching. I intervene for 15 years in one of the best establishments of Clermont Ferrand. While waiting for our contact, I wish you, madam, a great day. Respectful greetings
Statistics
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Mathematics, Statistics, PACES and IB students (Lyon)
Ceren
Excellent lesson and much enjoyed by my son. More lessons booked!
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