**Lecture 1**: These are the notes I prepared, but they contain more information than I ended up covering

**Lecture 2**: The complex exponential function

**Lecture 3**: The (complex) Fourier series for a periodic function

**Lecture 4**: The complex Fourier coefficients as dot products. Intro to the discrete Fourier transform.

**Lecture 5**: The discrete Fourier transform

**Lecture 8**: Overview and the fast Fourier transform

**Lecture 9**: Waveforms vs wavelets. Intro to the Haar wavelet transform.

**Lecture 10**: The philosophy of wavelets, and more details on the Haar transform.

**Lecture 11**: The CDF(2,2) Wavelet transform.

**Lecture 12**: Haar Wavelet examples on epidemiological data. Overview of wavelet design considerations.

**Lecture 13**: What makes a good wavelet?

**pre-Lecture 14**: The CDF(2,2) Wavelet

**Lecture 14**: The CDF(2,2) Wavelet

**Lecture 15**: The linear algebra of wavelet transformations

**pre-Lecture 16**: The Daub4 Wavelet

**Lecture 16**: The Daub4 Wavelet

**pre-Lecture 17**: Scaling and wavelet vectors

**Lecture 17**: Scaling and wavelet vectors

**Lecture 19**: Intro to 2-D wavelet transforms, and overview of filter banks.

**Lecture 20**: 2-D wavelet transforms.

**Lecture 21**: 2-D wavelet transforms.

**Lecture 22**: Wavelet transforms for unbounded signals, part 1.

**Lecture 23**: Wavelet transforms for unbounded signals, part 2.