Adaptive and array signal processing (tutoring)

I am looking for people who can teach me below topics. 1. Adaptive Filters - Single channel adaptive equalization (temporal filter) - Multi channel adaptive beamforming (spatial filter) 2 Mathematical Background 2.1 Calculus - Gradients - Differentiation with respect to a complex vector - Quadratic optimization with linear constraints (method of Lagrangian multipliers) 2.2 Stochastic processes - Stationary processes - Time averages - Ergodic processes - Correlation matrices 2.3 Linear algebra - Eigenvalue decomposition - Eigenfilter - Linear system of equations - Four fundamental subspaces - Singular value decomposition - Generalized inverse of a matrix - Projections - Low rank modeling 3 Adaptive Filters 3.1 Linear Optimum Filtering (Wiener Filters) - Principle of Orthogonality - Wiener-Hopf equations - Error-performance surface - MMSE (minimum mean-squared error) - Canonical form of the error-performance surface - MMSE filtering in case of linear Models 3.2 Linearly Constrained Minimum Variance Filter - LCMV beamformer - Minimum Variance Distortionless Response (MVDR) spectrum: Capon's method - LCMV beamforming with multiple linear constraints 3.3 Generalized Sidelobe Canceler 3.4 Iterative Solution of the Normal Equations - Steepest descent algorithm - Stability of the algorithm - Optimization of the step-size 3.5 Least Mean Square (LMS) Algorithm 3.6 Recursive Least Squares (RLS) Algorithm 4 High-Resolution Parameter Estimation - Data model (DOA estimation) - Eigendecomposition of the spatial correlation matrix at the receive array - Subspace estimates - Estimation of the model order 4.1 Spectral MUSIC - DOA estimation - Example: uniform linear array (ULA) - Root-MUSIC for ULAs - Periodogram - MVDR spatial spectrum estimation (review) 4.2 Standard ESPRIT - Selection matrices - Shift invariance property 4.3 Signal Reconstruction - LS solution - MVDR / BLUE solution - Wiener solution (MMSE solution) - Antenna patterns 4.4 Spatial smoothing 4.5 Forward-backward averaging 4.6 Real-valued subspace estimation 4.7 1-D Unitary ESPRIT - Reliability test - Applications in Audio Coding 4.8 Multidimensional Extensions - 2-D MUSIC - 2-D Unitary ESPRIT - R-D Unitary ESPRIT 4.9 Multidimensional Real-Time Channel Sounding 4.10 Direction of Arrival Estimation with Hexagonal ESPAR Arrays 5 Tensor-Based Signal Processing 5.1 Introduction and Motivation 5.2 Fundamental Concepts of Tensor Algebra 5.3 Elementary Tensor Decompositions - Higher Order SVD (HOSVD) - CANDECOMP / PARAFAC (CP) Decomposition 5.4 Tensors in Selected Signal Processing Applications 6 Maximum Likelihood Estimators 6.1 Maximum Likelihood Principle 6.2 The Fisher Information Matrix and the Cramer Rao Lower Bound (CRLB) - Efficiency - CRLB for 1-D direction finding applications - Asymptotic CRLB