Courses   Aims
 Advance Time Series(SPRING_2017)
Spectral analysis. Properties of Fourier transforms, spectral representations of autocovariance functions and stationary time series, Spectral inference. The periodogram and its properties, estimation of spectral densities, Multivariate time series. Cross covariance and the cross-spectrum, multivariate ARMA processes, stationary time series and model fitting, Wavelets and its application in time series
 Time Series (I)(SPRING_2017,2016)  Introduction to Time series and Forecasting
 Statistical Technique(FALL-SPRING_2016,2015,2014,2013)
Linear Methods for REGRESSION 5weeks ,Diagnostic checking of Hypotheses on Linear Regression,Multiple Regression,Subset selection,PCA, Shrinkage Methods Ridge Regression, PCR Lasso Regression, Linear Methods for Classification, Introduction, Nearest Neighbor, Linear Regression of an indicator Matrix,Linear Discriminant Analysis, Quadratic Discriminant Analysis, Bayes Classifier Logistic Regression,  Introduction, Piecewise Polynomials and Splines, Natural Cubic Splines Smoothing Splines, Nonparametric Logistic Regression , Wavelet Smoothing , Kernel Methods :3 weeksOne Dimensional Kernel Smoothers, Local Linear Regression, Local Polynomial Regression, Local Likelihood, Kernel Density Estimation
 Introduction to Statistical Learning

based on Elements of Statistical Learning

 Probability (I)(FALL_2015)
Random variable: discrete and continuousExpectation and variance and probability distribution 5weeks, Distribution function of a function of random variable- Joint distribution function-Density function- Independent variables 2 weeks, Known distribution in discrete and continuous variables 6weeks, Correlation- conditional distribution- Conditional density and expectation 3weeks, Law of large number: strong and weak-Central limit theorem 1 weeks
 Introduction to probability and application