Library in Signal Processing: Signal Processing Theory and Machine Learning

1 - Introduction to Signal Processing Theory
2 - Continuous-Time Signals and Systems
3 - Discrete-Time Signals and Systems
4 - Random Signals and Stochastic Processes
5 - Sampling and Quantization
6 - Digital Filter Structures and Their Implementation
7 - Multirate Signal Processing for Software Radio Architectures
8 - Modern Transform Design for Practical Audio/Image/Video Coding Applications
9 - Discrete Multi-Scale Transforms in Signal Processing
10 - Frames in Signal Processing
11 - Parametric Estimation
12 - Adaptive Filters
13 - Introduction to Machine Learning
14 - Learning Theory
15 - Neural Networks
16 - Kernel Methods and Support Vector Machines
17 - Online Learning in Reproducing Kernel Hilbert Spaces
18 - Introduction to Probabilistic Graphical Models
19 - A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering
20 - Clustering
21 - Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc.
22 - Semi-Supervised Learning
23 - Sparsity-Aware Learning and Compressed Sensing: An Overview
24 - Information Based Learning
25 - A Tutorial on Model Selection
26 - Music Mining