Différences

Ci-dessous, les différences entre deux révisions de la page.

--- teaching:methcalchim:start [2020/06/02 03:08] – [Classical numerical methods] villersd
+++ teaching:methcalchim:start [2021/01/22 12:41] – villersd
@@ Ligne 45: / Ligne 45: @@
     * Diagonalisation and triangularisation
     * LU decomposition : factorization in triangular matrices
-  * [[numerical_integration|Numerical intégration]] (integrals)
-    * Simpson method and gaussian quadratures
   * [[root-finding_algorithm|Root findings :  equations f(x) = 0]]
     * Polynomial equations
@@ Ligne 52: / Ligne 50: @@
     * Secant method, Regula falsi
     * Newton-Raphson method
+  * [[numerical_integration|Numerical intégration]] (integrals)
+    * Simpson method and gaussian quadratures
+<note tip>
+Learning outcomes :
+  * Systems of linear equations
+    * failing of the theoretical way to solve a linear system using determinant and cofactors (np complexity)
+    * triangularisation and diagonalisation principles : algorithm and complexity
+    * "divide by zero" errors and pivot solutions
+    * extension towards the matrix inversion
+    * lower-upper LU decomposition and complexity (N³ for the decomposition step and N² for substitution step). How to solve systems with varying independant vectors
+    * special matrix require special algorithms : tridiagonal matrix algorithm (Thomas algorithm)
+  * Root findings
+  * Numerical intégration
+</note>
 ==== Classical numerical methods ====
@@ Ligne 177: / Ligne 190: @@
   * [[https://medium.freecodecamp.org/an-overview-of-the-gradient-descent-algorithm-8645c9e4de1e|An overview of the Gradient Descent algorithm]]
   * [[https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8|Building A Logistic Regression in Python, Step by Step]]
+  * [[https://medium.com/swlh/building-linear-regression-in-python-75a429b0d3ba|Building Linear Regression in Python]]
 ----