Lecture 1: Time/Frequency Analysis
· Fourier analysis.
· Windowed Fourier transform.
· Wavelet transform.

Lecture 2: Fast Algorithms and Applications
· Multiresolution Analysis.
· Filter banks.
· Lifting schemes.
· Signal/Image compression, denoising.

Lecture 3: Main Characters
· Pre-wavelets: splines, orthogonal polynomials, etc.
· Wavelets: Haar, Meyer, Daubechies, Coiflets, symmlets, etc.
· Post-wavelets: brushlets, edgelets, ridgelets, etclets.
· Beyond wavelets.

Lecture 4: Variations over a theme
· Wavelet packets and local cosine bases.
· Biorthogonal wavelets.
· Wavelets on the interval.
· Multiwavelets.

Lecture 5: Applications to Signal/Image Processing
· Denoising and restoration.
· Image compression.
· Feature extraction.

Lecture 6: Advanced applications
· Block-denoising.
· Recovering the derivative from a noisy signal.
· Computing products of functions and matrices.
· Divergence-free wavelets.