CISC371, Nonlinear Data Analysis: Prerequisites


These sessions are brief summaries of prerequisite material for this course. These videos are not a substitute for reviewing the required course material. These sessions are intended to help you to recall some linear algebra that we assume that you already know.

Prerequisites for the course on linear data analysis, which are mainly linear algebra, are available below.

The lectures were produced using technology that is described in this video:

No.    Video
8 Overview of Prerequisites
9 Matrix Norms
10 Orthogonal Projection and Linear Regression
11 Data Standardization
12 SVD - Singular Value Decomposition
13 PCA - Principal Component Analysis
14 Unsupervised Learning - Clustering
15 Supervised Learning - Labels of Data
16 Classifier Assessment - Confusion Matrix
17 Classifier Assessment - ROC Curve
CISC271 Prerequisites
1 Overview of Prerequisites
2 Vectors
3 Vector Spaces
4 Linear Maps
5 Basis Vectors For A Vector Space
6 Square Matrices
7 Eigenvectors And Eigenvalues

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