01:31
Foundations of Artificial Intelligence and Machine Learning Course Promo Video
14:36
Convergence, Tracking, and the LMS Algorithm Step Size
12:07
Solving the Least-Squares Problem with Gradient Descent: the Least-Mean-Square Algorithm.
15:00
Finding the MMSE Filter Optimum Weights
13:14
Introduction to Minimum Mean-Squared-Error Filtering
11:46
Signals- The Basics
07:57
Matrix Completion
14:43
Network Graphs and Page Rank Algorithm
14:48
Eigendecomposition, Singular Value Decomposition, and Power Iterations
14:45
Bias-Variance Tradeoff in Low Rank Approximations
14:10
Principal Component Analysis
14:47
Singular Value Decomposition and Regularization of Least Squares Problems
15:51
The Singular Value Decomposition and Least Squares Problems
13:59
Properties of the Singular Value Decomposition
17:05
The Singular Value Decomposition
12:06
Clustering Data with the K Means Algorithm
14:41
Low Rank Decompositions of Matrices
21:09
Regularization and Ridge Regression for Supervised Learning
14:48
Complexity, Overfitting, and Cross Validation
19:18
Geometry of the Squared Error Surface
18:03
Solving the Least Squares Problem Using Gradients
17:01
Solving the Least-Squares Problem Using Geometry
11:33
Approximate Solutions, Norms, and the Least-Squares Problem
15:10
Representing Data with Bases
12:48
Subspaces in Machine Learning
12:57
Uniqueness of Solutions to Learning Problems
12:03
Linear Independence and Rank in Learning
17:58
Patterns in Data and Outer Products
08:59
Classifying Data and Matrix Multiplication
15:01
Fitting Models to Data and Matrix Multiplication
17:46
Representing Functions as Inner Products
09:48
The Machine Learning Process
07:45
Introduction to Machine Learning
07:40
The Two-Dimensional Discrete Cosine Transform
10:33
The Discrete Cosine Transform
13:01
The Two-Dimensional Discrete Fourier Transform
17:46
Kernel Regression
12:30
Kernel Based Support Vector Machines
14:57
The Backpropagation Algorithm for Training Neural Networks
10:30
Introduction to Neural Networks
14:31
Stochastic Gradient Descent
10:46
Gradient Descent for Support Vector Machines and Subgradients
13:58
Support Vector Machines for Classification
12:01
Hinge Loss for Binary Classifiers
11:12
Solving l1 Regularized Least Squares via Proximal Gradient Descent
10:58
Sparse Solutions to Least Squares Problems Using the LASSO
14:58
Proximal Gradient Descent Algorithms
13:41
Gradient Descent Solutions to Least Squares Problems
11:21
Two-Dimensional Signal Processing
13:29
The Spectrum of the Output of a System
15:54
Frequency Response of the Cascade of Filters and Notch Filters
15:14
Frequency Response of Finite-Impulse Response Systems
08:14
Example of Graphical Discrete-Time Convolution
07:10
The Impulse Response and Convolution
11:13
Linear, Time-Invariant, and Causal Systems
07:25
The Impulse Response of Systems
10:50
Digital Signal Processing Systems
13:15
Computing the Spectrum of Sampled Signals with the Discrete Fourier Transform
15:14
The Discrete Fourier Transform
07:39
Amplitude Quantization in Analog to Digital Conversion
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About
My All Signal Processing channel contains short lectures on topics in signal processing. Many of the lectures have also been used with an inverted or "flipped" classroom paradigm at the University of Wisconsin.
The playlists provide a systematic progression through the material. Each playlist includes a list of prerequisite playlists for understanding the background material.
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