Update week48.do.txt

Author: mhjensen

doc/src/week48/week48.do.txt

@@ -1,5 +1,5 @@
-TITLE: Week 48: Support Vector Machines and Summary of course
-AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo, Norway
+TITLE: Week 48: Gradient boosting  and summary of course
+AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics and Center for Computing in Science Education, University of Oslo, Norway
 DATE: today
 
 !split
@@ -14,7 +14,7 @@ DATE: today
 !eblock  
 
 !bblock Plans for the lecture Monday 25 November, with video suggestions etc
-o Bossting and gradient boosting and ensemble models
+o Boosting and gradient boosting and ensemble models
 o Summary of course
 o Readings and Videos:
   o These lecture notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week47/ipynb/week48.ipynb"
@@ -852,7 +852,7 @@ o Central elements from linear algebra, matrix inversion and SVD
 o Gradient methods for data optimization
 o Estimation of errors using cross-validation, bootstrapping and jackknife methods;
 o Practical optimization using Singular-value decomposition and least squares for parameterizing data.
-o Principal Component Analysis to reduce the number of features.
+o Not discussed: Principal Component Analysis to reduce the number of features.
 
 !split
 ===== Machine learning =====
@@ -872,7 +872,7 @@ o Decisions trees and ensemble methods:
   o Bagging and voting
   o Random forests
   o Boosting and gradient boosting
-o Support vector machines
+o Not discussed this year: Support vector machines
   o Binary classification and multiclass classification
   o Kernel methods
   o Regression
@@ -894,19 +894,19 @@ ethical conduct is emphasized throughout the course.
 * Understand linear methods for regression and classification;
 * Learn about neural network;
 * Learn about bagging, boosting and trees
-* Support vector machines
+#* Support vector machines
 * Learn about basic data analysis;
 * Be capable of extending the acquired knowledge to other systems and cases;
 * Have an understanding of central algorithms used in data analysis and machine learning;
-* Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.
+* Work on numerical projects to illustrate the theory. The projects play a central role.
 
 
 
 !split
 ===== Perspective on Machine Learning =====
 
 o Rapidly emerging application area
-o Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.
+o Experiment AND theory are evolving in many many fields. 
 o Requires education/retraining for more widespread adoption
 o A lot of “word-of-mouth” development methods
 
@@ -933,7 +933,7 @@ o "Follow ML on ArXiv":"https://arxiv.org/list/cs.LG/recent"
 
 o Identify problem type: classification, regression
 o Consider your data carefully
-o Choose a simple model that fits 1. and 2.
+o Choose a simple model that fits 1 and 2
 o Consider your data carefully again! Think of data representation more carefully.
 o Based on your results, feedback loop to earliest possible point
 
@@ -962,9 +962,9 @@ o  When to do train/test split?
 
 
 !split
-=====  Which Activation and Weights to Choose in Neural Networks =====
+=====  Which activation and weights to choose in neural networks =====
 
-o RELU? ELU?
+o RELU? ELU? GELU? etc
 o Sigmoid or Tanh?
 o Set all weights to 0?
   * Terrible idea