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Morten Hjorth-Jensen
2024-11-23 22:10:16 +01:00
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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
@@ -894,11 +894,11 @@ 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.
@@ -906,7 +906,7 @@ ethical conduct is emphasized throughout the course.
===== 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