Bagging and boosting are ensemble techniques that combine multiple machine learning models to improve performance. Bagging generates new training data sets by sampling the original data with replacement and trains models on these new data sets. The predictions from all models are averaged or voted on to make the final prediction. Boosting sequentially trains models where each new model focuses on samples the previous model misclassified. This process adjusts the weights of misclassified samples so later models focus more on difficult cases. Both techniques help reduce variance compared to a single model and can improve performance, especially for unstable models like decision trees and neural networks.
This is a courseware on Algebraic Expression intended for high school teachers and students. It covers the concept and basic operations on algebraic expressions.
Contacts Details:
Mobile: +233 248870038
Email: ddeynu@aims.edu.gh, kwabla1991@gmail.com
This is a courseware on Algebraic Expression intended for high school teachers and students. It covers the concept and basic operations on algebraic expressions.
Contacts Details:
Mobile: +233 248870038
Email: ddeynu@aims.edu.gh, kwabla1991@gmail.com
Introduction to machine learning terminology.
Applications within High Energy Physics and outside HEP.
* Basic problems: classification and regression.
* Nearest neighbours approach and spacial indices
* Overfitting (intro)
* Curse of dimensionality
* ROC curve, ROC AUC
* Bayes optimal classifier
* Density estimation: KDE and histograms
* Parametric density estimation
* Mixtures for density estimation and EM algorithm
* Generative approach vs discriminative approach
* Linear decision rule, intro to logistic regression
* Linear regression
Understanding Blackbox Prediction via Influence FunctionsSEMINARGROOT
Pang Wei Koh and Percy Liang
"Understanding Black-Box prediction via influence functions" ICML 2017 Best paper
References:
https://youtu.be/0w9fLX_T6tY
https://arxiv.org/abs/1703.04730
Introduction to machine learning terminology.
Applications within High Energy Physics and outside HEP.
* Basic problems: classification and regression.
* Nearest neighbours approach and spacial indices
* Overfitting (intro)
* Curse of dimensionality
* ROC curve, ROC AUC
* Bayes optimal classifier
* Density estimation: KDE and histograms
* Parametric density estimation
* Mixtures for density estimation and EM algorithm
* Generative approach vs discriminative approach
* Linear decision rule, intro to logistic regression
* Linear regression
Understanding Blackbox Prediction via Influence FunctionsSEMINARGROOT
Pang Wei Koh and Percy Liang
"Understanding Black-Box prediction via influence functions" ICML 2017 Best paper
References:
https://youtu.be/0w9fLX_T6tY
https://arxiv.org/abs/1703.04730
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
3. Bagging
Bootstrap Model
Randomly generate L set of cardinality N from the original
set Z with replacement.
Corrects the optimistic bias of R-Method
"Bootstrap Aggregation"
Create Bootstrap samples of a training set using sampling
with replacement.
Each bootstrap sample is used to train a different
component of base classifier
Classification is done by plurality voting
11. Why does bagging work ?
Main reason for error in learning is due to noise ,bias and
variance.
Noise is error by the target function
Bias is where the algorithm can not learn the target.
Variance comes from the sampling, and how it affects the
learning algorithm
Does bagging minimizes these errors ?
Yes
Averaging over bootstrap samples can reduce error from
variance especially in case of unstable classifiers
12. Bagging
In fact Ensemble reduces variance
Let f(x) be the target value of x and h1 to hn
be the set of base hypotheses and h-
average be the prediction of base
hypotheses
E(h,x) = (f(x) – h(x))^2 Squared Error
13. Ensemble Reduces variance
Let f(x) be the target value for x.
Let h1, . . . , hn be the base hypotheses.
Let h-avg be the average prediction of h1, .
. . , hn.
Let E(h, x) = (f(x) −h(x))2
Is there any relation between h-avg and
variance?
yes
14. E(h-avg,x) = ∑(i = 1 to n)E(hi ,x)/n
∑(i = 1 to n) (hi(x) – h-avg(x))^2/n
That is squared error of the average prediction
equals the average squared error of the base
hypotheses minus the variance of the base
hypotheses.
Reference – 1-End of the slideshow.
15. Bagging - Variants
Random Forests
A variant of bagging proposed by Breiman
It’s a general class of ensemble building methods using
a decision tree as base classifier.
Classifier consisting of a collection of tree-structure
classifiers.
Each tree grown with a random vector Vk where k = 1,…L
are independent and statistically distributed.
Each tree cast a unit vote for the most popular class at input
x.
16. Boosting
□Atechnique for combining multiple base classifiers whose
combined performance is significantly better than that of any
of the base classifiers.
Sequential training of weak learners
Each base classifier is trained on data that is weighted
based on the performance of the previous classifier
Each classifier votes to obtain a final outcome
18. Boosting - Hedge(β)
Boosting follows the model of online algorithm.
Algorithm allocates weights to a set of strategies and
used to predict the outcome of the certain event
After each prediction the weights are redistributed.
Correct strategies receive more weights while the weights
of the incorrect strategies are reduced further.
Relation with Boosting algorithm.
Strategies corresponds to classifiers in the ensemble and
the event will correspond to assigning a label to sample
drawn randomly from the input.
20. Boosting - AdaBoost
Start with equally weighted data, apply first classifier
Increase weights on misclassified data, apply second
classifier
Continue emphasizing misclassified data to subsequent
classifiers until all classifiers have been trained
23. Margin Theory
Testing error continues to decrease
Ada-boost brought forward margin theory
Margin for an object is related to certainty of
its classification.
Positive and large margin – correct
classification
Negative margin - Incorrect Classification
Very small margin – Uncertainty in
classification
24. Similar classifier can give different label to
an input.
Margin of object x is calculated using the
degree of support.
Where
25. Freund and schapire proved upper bounds
on the testing error that depend on the
margin
Let H a finite space of base classifiers.For
delta > 0 and theta > 0 with probability at
least 1 –delta over the random choice of the
training set Z, any classifier ensemble D
{D1, . . . ,DL} ≤ H combined by the weighted
average satisfies
26. P(error ) = probability that the ensemble will
make an error in labeling x drawn randomly
from the distribution of the problem
P(training margin < theta ) is the probabilty that
the margin for a randomly drawn data point
from a randomly drawn training set does not
exceed theta
27. Thus the main idea for boosting is to
approximate the target by approximating
the weight of the function.
These weights can be seen as the min-max
strategy of the game.
Thus we can apply the notion of game
theory for ada-boost.
This idea has been discussed in the paper
of freund and schpaire.
28. Experiment
PR Tools:
>> A = gendatb(500, 1);
>> [W,V,ALF] = adaboostc(A,qdc,20,[],1);
>> scatterd(A)
>> plotc(W)
□
Uses Quadratic Bayes Normal Classifier with default
settings, 20 iterations.
29. Example
AdaBoost: QDC
Each QDC classification boundary
(black), Final output (red)
Final output of AdaBoost with 20
QDC classifiers
33. References
1 - A. Krogh and J. Vedelsby (1995).Neural
network ensembles, cross validation and
activelearning. In D. S. Touretzky G.
Tesauro and T. K. Leen, eds., Advances in
Neural Information Processing Systems, pp.
231-238, MIT Press.