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DSP Lecture 21: Gradient descent and LMS
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Classroom Contents
Digital Signal Processing Lectures, Fall 2014
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- 1 DSP Lecture 1: Signals
- 2 DSP Lecture 2: Linear, time-invariant systems
- 3 DSP Lecture 3: Convolution and its properties
- 4 DSP Lecture 4: The Fourier Series
- 5 DSP Lecture 5: the Fourier Transform
- 6 DSP Lecture 6: Frequency Response
- 7 DSP Lecture 7: The Discrete-Time Fourier Transform
- 8 DSP Lecture 8: Introduction to the z-Transform
- 9 DSP Lecture 9: Inverse z-Transform; Poles and Zeros
- 10 DSP Lecture 10: The Discrete Fourier Transform
- 11 DSP Lecture 10a: Exam 1 Review
- 12 DSP Lecture 11: Radix-2 Fast Fourier Transforms
- 13 DSP Lecture 12: The Cooley-Tukey and Good-Thomas FFTs
- 14 DSP Lecture 13: The Sampling Theorem
- 15 DSP Lecture 14: Continuous-time filtering with digital systems; upsampling and downsampling
- 16 DSP Lecture 15: Multirate signal processing and polyphase representations
- 17 DSP Lecture 16: FIR filter design using least-squares
- 18 DSP Lecture 17: FIR filter design (Chebyshev)
- 19 DSP Lecture 18: IIR filter design
- 20 DSP Lecture 19: Introduction to adaptive filtering; ARMA processes
- 21 DSP Lecture 20: The Wiener filter
- 22 DSP Lecture 22a: Exam 2 format/review
- 23 DSP Lecture 21: Gradient descent and LMS
- 24 DSP Lecture 22: Least squares and recursive least squares
- 25 DSP Lecture 23: Introduction to quantization
- 26 DSP Lecture 24: Differential quantization and vocoding
- 27 DSP Lecture 25: Perfect reconstruction filter banks and intro to wavelets
- 28 DSP Lecture 1a: Matlab for DSP; introduction to Cody Coursework
