jagomart
digital resources
picture1_Matrix Pdf 173880 | Lecture01 Linear


 167x       Filetype PDF       File size 0.82 MB       Source: engineering.purdue.edu


File: Matrix Pdf 173880 | Lecture01 Linear
ece595 stat598 machine learning i lecture 01 linear regression spring 2020 stanley chan school of electrical and computer engineering purdue university c stanley chan 2020 all rights reserved 1 22 ...

icon picture PDF Filetype PDF | Posted on 27 Jan 2023 | 2 years ago
Partial capture of text on file.
                 ECE595 / STAT598: Machine Learning I
                          Lecture 01: Linear Regression
                                            Spring 2020
                                           Stanley Chan
                            School of Electrical and Computer Engineering
                                         Purdue University
                                                                              c
                                                                             
Stanley Chan 2020. All Rights Reserved.
                                                                                              1/22
   Outline
                                                                             c
                                                                            
Stanley Chan 2020. All Rights Reserved.
                                                                                             2/22
   Outline
   Mathematical Background
         Lecture 1: Linear regression: A basic data analytic tool
         Lecture 2: Regularization: Constraining the solution
         Lecture 3: Kernel Method: Enabling nonlinearity
   Lecture 1: Linear Regression
         Linear Regression
               Notation
               Loss Function
               Solving the Regression Problem
         Geometry
               Projection
               Minimum-Norm Solution
               Pseudo-Inverse
                                                                             c
                                                                            
Stanley Chan 2020. All Rights Reserved.
                                                                                             3/22
   Basic Notation
           Scalar: a,b,c ∈ R
           Vector: ❛,❜,❝ ∈ Rd
           Matrix: ❆,❇,❈ ∈ RN×d; Entries are a or [❆] .
                                                                      ij          ij
           Rows and Columns
                           |        |             |                         — (①1)T                —
                                                                              — (①2)T                —
                   ❆=❛ ❛ ... ❛ , and ❆=                                                                .
                               1     2              d                                      .             
                                                                                           .             
                              |      |             |                                        .
                                                                                           N T
                                                                                 — (① )               —
           {❛j}: The j-th feature. {①n}: The n-th sample.
           Identity matrix ■
           All-one vector 1 and all-zero vector 0
           Standard basis ❡i.
                                                                                              c
                                                                                             
Stanley Chan 2020. All Rights Reserved.
                                                                                                                 4/22
The words contained in this file might help you see if this file matches what you are looking for:

...Ece stat machine learning i lecture linear regression spring stanley chan school of electrical and computer engineering purdue university c all rights reserved outline mathematical background a basic data analytic tool regularization constraining the solution kernel method enabling nonlinearity notation loss function solving problem geometry projection minimum norm pseudo inverse scalar b r vector rd matrix rn d entries are or ij rows columns t n j th feature sample identity one zero standard basis...

no reviews yet
Please Login to review.