├── MATLAB-10 ├── BATcs_10_14.m ├── MRIcs_10_13.m ├── OMPCSMP_10_12.m ├── mixed_calls.wav ├── multitone_10_11.m └── phant.mat ├── MATLAB-11 ├── Exercise 11.19 (Kernel Ridge Regression) │ ├── BladeRunner.wav │ ├── kappa.m │ ├── kernel_regression_l2_l2_biased.m │ ├── kernel_regression_l2_l2_unbiased.m │ └── kernelridge_11_19.m ├── Exercise 11_20 (Suport Vector Regression) │ ├── BladeRunner.wav │ ├── SMO_regression2.m │ ├── kappa.m │ └── supportvectorregression_11_20.m ├── Exercise 11_21 (Support Vector Machines) │ ├── SMO_classification.m │ ├── kappa.m │ └── supportvectormachine_11_21.m ├── Exercise 11_22 (Online Learning in RKHS) │ ├── Kappa.m │ ├── NORMA_L2.m │ ├── QKernel_APSM.m │ ├── QKernel_NLMS.m │ ├── compute_subgrad_coef.m │ ├── fast_compute_function_norm.mexw64 │ ├── fast_kernel.mexw64 │ ├── fast_real_output_kernel_computation.mexw64 │ ├── loss_epsilon.m │ ├── non_zero_cardinality.m │ ├── nonlinchaneqNonstationary_11_22b.m │ ├── nonlinchaneqstationary_11_22a.m │ └── sum_mean_val2.m └── Exercise 11_23 (Authorship Identification) │ ├── JInsect.jar │ ├── NGGSVMKernel.jar │ ├── OpenJGraph.jar │ └── authorship_identification_11_23.m ├── MATLAB-12 ├── EMregression_12_16.m ├── Gaussmix_12_17.m ├── MoE_12_19.m ├── error_ellipse.m ├── k_means.m ├── kmean_EM_12_18.m └── prediction_12_15.m ├── MATLAB-14 ├── Gibbssample_14_16.m ├── casestudy_14_11.m └── randgen.m ├── MATLAB-17 ├── APF_17_10.m ├── GPF_17_7.m ├── SIR_17_9.m ├── SIS_17_8.m ├── stochvolatility_17_11.m └── tracking_17_12.m ├── MATLAB-18 ├── Exercise 18_13 (Neural Network tarining-1) │ └── neuralnetwork_18_13.m ├── Exercise 18_14 (Neural Network training-2) │ └── neuralnetwork_18_14.m └── Exercise 18_15 (Neural Network training-2) │ └── neuralnetwork_18_15.m ├── MATLAB-19 ├── ICA_19_11.m ├── Matlab19_10.m ├── Matlab19_9.m ├── dictionarylearn_19_12.m ├── music.wav ├── robustPCA19_13.m ├── voice1.wav └── voice2.wav ├── MATLAB-4 ├── boat_original.eps ├── boat_original.gif ├── channel_equalization_4_27.m ├── debluring_4_25.m ├── kalman_4_28.m └── noise_cancelation_4_26.m ├── MATLAB-5 ├── APA_5_22.m ├── LMSWhiteAR_5_20.m ├── LMS_distrib_ATC.m ├── LMS_distrib_CTA.m ├── LMScorelated_5_20.m ├── LMSwhitenoise_5_20.m ├── RobbinMonro_5_19.m ├── TDLMS_5_21.m ├── decision_feedback_equalizer_5_23.m ├── distributed_LMS_5_24.m ├── grad_descent_contours_5_18.m ├── grad_descent_errplot_5_18.m └── makenetwork.m ├── MATLAB-6 ├── RLS_6_20.m ├── RLStracking_6_21.m ├── tls.m └── totalLS_6_22.m ├── MATLAB-7 ├── __MACOSX │ └── ._naivebayes_7_20.m ├── boosting_7_23.m ├── boostingexamplebook_7_5.m ├── classification_7_19.m ├── decisiontree_7_22.m ├── logisitcregr_7_21.m ├── naivebayes_7_20.m └── proteinfolding_7_24.m ├── MATLAB-8 ├── APSM_8_38.m ├── APSM_distrib.m ├── LMS_distrib_ATC.m ├── LMS_distrib_CTA.m ├── distributed_APSM_LMS_8_39.m ├── makenetwork.m └── pegasos_8_40.m ├── MATLAB-9 ├── boats.mat ├── denoising_9_22.m ├── sparse_9_20.m └── sparse_9_21.m ├── Machine Learning A 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