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1Mit Hilfe von Daten das Lernen optimieren
Episode 3 | Oktober 2022 | Dr. Natalie Borter Ein Podcast der Phil.-hum. Fakultät der Universität Bern
2 years ago by @ereidt
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data
enhancement
highered
learning
learninganalytics
podcast
scalabilty
studentsuccess
dataenhancementhigheredlearninglearninganalyticspodcastscalabiltystudentsuccess
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1The Predictive Privacy Project - Rainer Mühlhoff
Data Protection in the Context of Big Data and AI
2 years ago by @hotho
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Datenschutz
big
data
deep
learning
machine
ml
predictive
privacy
Datenschutzbigdatadeeplearningmachinemlpredictiveprivacy
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1Google AI Blog: KELM: Integrating Knowledge Graphs with Language Model Pre-training Corpora
https://ai.googleblog.com/2021/05/kelm-integrating-knowledge-graphs-with.html
2 years ago by @hotho
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KG
LM
data
dataset
deep
integration
learning
model
KGLMdatadatasetdeepintegrationlearningmodel
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1Data and learning: Does more = better?
Blogpost by Neil Mosley
2 years ago by @ereidt
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complexity
data
digitalisation
edtech
evidence
learning
learninganalytics
potentials
teaching
transformation
complexitydatadigitalisationedtechevidencelearninglearninganalyticspotentialsteachingtransformation
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13rd International Conference on Advances in Artificial Intelligence Techniques (ArIT 2022)
http://https://www.cseit2022.org/arit/index
2 years ago by @leninsha
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data
learning
machine
mining
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datalearningmachineminingneural
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1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube
https://www.youtube.com/watch?v=hOfcOak-moU&t=1s
3 years ago by @becker
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data
dataset
graph
representation
learning
biomedical
medical
discovery
subgraph
graphs
machine
network
networks
datadatasetgraphrepresentationlearningbiomedicalmedicaldiscoverysubgraphgraphsmachinenetworknetworks
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2CORNELL CS4780 "Machine Learning for Intelligent Systems" - YouTube
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
3 years ago by @becker
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boosting
lecture
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learning
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weinberger
kilian
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119: Boosting
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
3 years ago by @becker
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4How to pick more beautiful colors for your data visualizations | Chartable
https://blog.datawrapper.de/beautifulcolors/
4 years ago by @becker
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color
choose
chose
graphics
colors
beautiful
data
science
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learning
paper
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visualize
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picking
picker
colorchoosechosegraphicscolorsbeautifuldatasciencemachinelearningpaperpapersvisualizationvisualizepickpickingpicker
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1hurcy/awesome-ehr-deeplearning: Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.
https://github.com/hurcy/awesome-ehr-deeplearning
4 years ago by @becker
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awesome
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1mims-harvard/scikit-fusion: scikit-fusion: Data fusion via collective latent factor models
https://github.com/mims-harvard/scikit-fusion
4 years ago by @becker
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data
fusion
mit
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graph
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python
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matrix
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factor
datafusionmitmultiviewlearningmachinegraphgraphsnetworkpythonsklearnskfusionscikitmatrixfactorizationgpupyheterogeneouslatentfactor
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1Learning analytics in a nutshell - YouTube
A short introduction to learning analytics by Yi-Shan Tsai
4 years ago by @ereidt
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data
ethics
improvement
learning
learninganalytics
management
people
support
video
dataethicsimprovementlearninglearninganalyticsmanagementpeoplesupportvideo
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2Statistical Significance Tests for Comparing Machine Learning Algorithms
https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/
4 years ago by @becker
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statistical
significance
test
testing
hypothesis
comparison
machine
learning
data
mining
science
cross
validation
statisticalsignificancetesttestinghypothesiscomparisonmachinelearningdataminingsciencecrossvalidation
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1How to use Data Scaling Improve Deep Learning Model Stability and Performance
https://machinelearningmastery.com/how-to-improve-neural-network-stability-and-modeling-performance-with-data-scaling/
4 years ago by @becker
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deep
learning
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output
data
machine
project
butterfly
deeplearningscalinginputoutputdatamachineprojectbutterfly
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1Don’t Sweat the Solver Stuff - Towards Data Science
https://towardsdatascience.com/dont-sweat-the-solver-stuff-aea7cddc3451
4 years ago by @becker
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logistic
regression
sklearn
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model
machine
learning
data
science
solver
python
py
logisticregressionsklearnlinearmodelmachinelearningdatasciencesolverpythonpy
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1arvkevi/kneed: Knee point detection in Python
https://github.com/arvkevi/kneed
4 years ago by @becker
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elbow
plot
method
clustering
number
of
clusters
cluster
python
data
science
mining
machine
learning
knee
kneed
kneedle
elbowplotmethodclusteringnumberofclustersclusterpythondatascienceminingmachinelearningkneekneedkneedle
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1scikit learn - logistic regression python solvers' defintions - Stack Overflow
https://stackoverflow.com/questions/38640109/logistic-regression-python-solvers-defintions
4 years ago by @becker
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logistic
regression
sklearn
solver
solvers
python
machine
learning
linear
data
science
logisticregressionsklearnsolversolverspythonmachinelearninglineardatascience
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1machine learning - Getting a low ROC AUC score but a high accuracy - Stack Overflow
https://stackoverflow.com/questions/47104129/getting-a-low-roc-auc-score-but-a-high-accuracy/47111246#47111246
4 years ago by @becker
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machine
learning
auc
acc
roc
data
mining
science
ml
accuracy
precision
recall
evaluation
metric
metrics
comparison
machinelearningaucaccrocdataminingsciencemlaccuracyprecisionrecallevaluationmetricmetricscomparison
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1EthicalML/awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
https://github.com/EthicalML/awesome-production-machine-learning
5 years ago by @becker
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machine
learning
ml
list
data
science
libraries
systems
machinelearningmllistdatasciencelibrariessystems
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1[D] How do you manage your machine learning experiments? : MachineLearning
https://www.reddit.com/r/MachineLearning/comments/bx0apm/d_how_do_you_manage_your_machine_learning/
5 years ago by @becker
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ml
machine
learning
tracking
monitoring
managing
data
science
projects
model
productivity
mlflow
sacred
logger
logging
mlmachinelearningtrackingmonitoringmanagingdatascienceprojectsmodelproductivitymlflowsacredloggerlogging
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2Prediction of user listening contexts for music playlists
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
29 days ago by @sop2-ffzg
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User
Multimodal
Semantic
language
Evaluation
Prediction
Data
aggregation
learning
analysis
Natural
Feature
from:kamber
Machine
Playlist
activities
curation
context
prediction
metrics
UserMultimodalSemanticlanguageEvaluationPredictionDataaggregationlearninganalysisNaturalFeaturefrom:kamberMachinePlaylistactivitiescurationcontextpredictionmetrics
(0)
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3Understanding User-Curated Playlists on Spotify: A Machine Learning Approach
M. Pichl, E. Zangerle, and G. Specht. Int. J. Multimed. Data Eng. Manag., 8 (4): 44–59 (Oct 1, 2017)
a month ago by @sop2-ffzg
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Analysis
Learning
Content
Music
Retrieval
Data
Information
from:kamber
Machine
User-Generated
AnalysisLearningContentMusicRetrievalDataInformationfrom:kamberMachineUser-Generated
(0)
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2Prediction of user listening contexts for music playlists
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
a month ago by @kamber
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Data
Evaluation
Feature
Machine
Multimodal
Natural
Playlist
Prediction
Semantic
User
activities
aggregation
analysis
context
curation
language
learning
metrics
prediction
DataEvaluationFeatureMachineMultimodalNaturalPlaylistPredictionSemanticUseractivitiesaggregationanalysiscontextcurationlanguagelearningmetricsprediction
(0)
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3Understanding User-Curated Playlists on Spotify: A Machine Learning Approach
M. Pichl, E. Zangerle, and G. Specht. Int. J. Multimed. Data Eng. Manag., 8 (4): 44–59 (Oct 1, 2017)
a month ago by @kamber
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Analysis
Content
Data
Information
Learning
Machine
Music
Retrieval
User-Generated
AnalysisContentDataInformationLearningMachineMusicRetrievalUser-Generated
(0)
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1Sentiment Analysis Classification of ChatGPT on Twitter Big Data in Indonesia Using Fast R-CNN
S. Pipin, F. Sinaga, S. Winardi, and M. Hakim. JURNAL MEDIA INFORMATIKA BUDIDARMA, 7 (4): 2137-2148 (2023)
a month ago by @siopipin
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Analysis
ChatGPT
Data
Deep
Fast
Learning
R-CNN
Sentiment
Twitter
myown
AnalysisChatGPTDataDeepFastLearningR-CNNSentimentTwittermyown
(0)
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1The current and future uses of machine learning in ecosystem service research
M. Scowen, I. Athanasiadis, J. Bullock, F. Eigenbrod, and S. Willcock. Science of The Total Environment, (December 2021)
9 months ago by @jascal_panetzky
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Big
Data-driven
Ecosystem
Machine
Methodology
Validation
data
ecomodelling
ecostudy
learning
modelling
services
BigData-drivenEcosystemMachineMethodologyValidationdataecomodellingecostudylearningmodellingservices
(0)
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4Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, and A. Ho. (2022)cite arxiv:2211.04325.
a year ago by @jaeschke
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data
dataset
learning
machine
ml
training
datadatasetlearningmachinemltraining
(0)
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1Instructional Resources and Access to Inclusive Education in Public Primary Schools in Trans Nzoia County, Kenya
B. Manasi. INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT, 6 (7): 1151-1155 (January 2023)
a year ago by @ijtsrd
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1840
275
384
48
96
County,
Data
Education
Head
In
Inclusive
Kenya.
Ministry
Needs
Nzoia
Policy
Special
Students
Teachers
The
This
Trans
While
a
access
accordance
all
analyzed
and
as
at
by
chosen
created
data
delivery
descriptive
design
document
education
education.
educators,
employed.
for
framework
free
from
gathered
group
head
high
how
impediments
in
included
inclusive
instruments.
interview
learning
like
looked
objectives,
of
offer
organization
pertinent
primary
public
purposeful
qualitative
quality
quantitative
questionnaires
random
reported
research
resources
respectively.
sampling
schedules
schools
selection,
simple
statistics
students.
study
study,
survey
target
teachers,
teaching
technique,
the
thematically
to
transcribed
using
was
were
with
18402753844896County,DataEducationHeadInInclusiveKenya.MinistryNeedsNzoiaPolicySpecialStudentsTeachersTheThisTransWhileaaccessaccordanceallanalyzedandasatbychosencreateddatadeliverydescriptivedesigndocumenteducationeducation.educators,employed.forframeworkfreefromgatheredgroupheadhighhowimpedimentsinincludedinclusiveinstruments.interviewlearninglikelookedobjectives,ofofferorganizationpertinentprimarypublicpurposefulqualitativequalityquantitativequestionnairesrandomreportedresearchresourcesrespectively.samplingschedulesschoolsselection,simplestatisticsstudents.studystudy,surveytargetteachers,teachingtechnique,thethematicallytotranscribedusingwaswerewith
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4Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
P. Villalobos, J. Sevilla, L. Heim, T. Besiroglu, M. Hobbhahn, and A. Ho. arXiv preprint arXiv:2211.04325, (2022)
a year ago by @becker
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themis
large
data
machine
learning
requirement
deep
models
model
dataset
datasets
set
sets
limit
themislargedatamachinelearningrequirementdeepmodelsmodeldatasetdatasetssetsetslimit
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2Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, and G. Varoquaux. Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, (2022)
a year ago by @becker
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citedby:scholar:count:5
citedby:scholar:timestamp:2022-12-1
comparison
data
decision
evaluation
forest
learning
machine
ml
network
neural
nn
performance
random
tabular
tree
trees
citedby:scholar:count:5citedby:scholar:timestamp:2022-12-1comparisondatadecisionevaluationforestlearningmachinemlnetworkneuralnnperformancerandomtabulartreetrees
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1Snorkel: rapid training data creation with weak supervision
A. Ratner, S. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Ré. The VLDB Journal, 29 (2): 709--730 (May 1, 2020)
2 years ago by @jaeschke
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data
labeling
learning
machine
ml
snorkel
supervision
training
datalabelinglearningmachinemlsnorkelsupervisiontraining
(0)
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1Identifying the Key Factors of Training Technical School and College Teachers in Bangladesh Using Data Mining
M. Rabbani. INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT, 6 (1): 864-869 (December 2021)
2 years ago by @ijtsrd
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&
Analysis
Bangladesh
Bayes
Bureau
Classifier
College
Data
Decision
Discriminant
Evaluation
Extra
Forest
Instructors
K
Key
Learning
Linear
Logistic
Machine
Mining
Naive
Neighbors
Processing
Random
Regression
School
Statistics
Teachers
Technical
Tree
Trees
factors
of
&AnalysisBangladeshBayesBureauClassifierCollegeDataDecisionDiscriminantEvaluationExtraForestInstructorsKKeyLearningLinearLogisticMachineMiningNaiveNeighborsProcessingRandomRegressionSchoolStatisticsTeachersTechnicalTreeTreesfactorsof
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1Longitudinal variational autoencoder
S. Ramchandran, G. Tikhonov, K. Kujanpää, M. Koskinen, and H. Lähdesmäki. International Conference on Artificial Intelligence and Statistics, page 3898--3906. PMLR, (2021)
2 years ago by @becker
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imputation
gain
missing
data
generative
model
net
nn
deep
learning
autoencoder
variational
longitudinal
temporal
timeseries
time
series
citedby:scholar:count:6
citedby:scholar:timestamp:2022-5-13
imputationgainmissingdatagenerativemodelnetnndeeplearningautoencodervariationallongitudinaltemporaltimeseriestimeseriescitedby:scholar:count:6citedby:scholar:timestamp:2022-5-13
(0)
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3Gain: Missing data imputation using generative adversarial nets
J. Yoon, J. Jordon, and M. Schaar. International conference on machine learning, page 5689--5698. PMLR, (2018)
2 years ago by @becker
show all tags
imputation
gain
missing
data
generative
model
adversarial
net
nn
deep
learning
citedby:scholar:count:468
citedby:scholar:timestamp:2022-5-13
imputationgainmissingdatagenerativemodeladversarialnetnndeeplearningcitedby:scholar:count:468citedby:scholar:timestamp:2022-5-13
(0)
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1Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysis of the Relation Between Economic Freedom and Quality of Life
J. Staufer, and P. Brockmann. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 7 (4): 15 (November 2018)
2 years ago by @leninsha
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Data
Development
Economic
Freedom
Human
Index
Learning
Machine
Mining
Networks
Neural
World
of
the
DataDevelopmentEconomicFreedomHumanIndexLearningMachineMiningNetworksNeuralWorldofthe
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5Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka, J. Patterson, and C. Nolet. Information, 11 (4): 193 (2020)
2 years ago by @becker
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ai
artifical
data
gpu
intelligence
learning
library
machine
paper:fastcor
py
python
rapids
science
aiartificaldatagpuintelligencelearninglibrarymachinepaper:fastcorpypythonrapidsscience
(0)
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3MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
T. Kyono, Y. Zhang, A. Bellot, and M. van der Schaar. Advances in Neural Information Processing Systems, (2021)
2 years ago by @becker
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causal
citedby:scholar:count:1
citedby:scholar:timestamp:2022-3-21
data
imputation
impute
learning
machine
missing
toread
value
causalcitedby:scholar:count:1citedby:scholar:timestamp:2022-3-21dataimputationimputelearningmachinemissingtoreadvalue
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2Developing Open Source Educational Resources for Machine Learning and Data Science
L. Bothmann, S. Strickroth, G. Casalicchio, D. Rügamer, M. Lindauer, F. Scheipl, and B. Bischl. (2021)cite arxiv:2107.14330Comment: 6 pages.
2 years ago by @jaeschke
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aiskills
data
learning
machine
ml
oer
science
aiskillsdatalearningmachinemloerscience
(0)
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3Probabilistic Active Learning in Datastreams
D. Kottke, G. Krempl, and M. Spiliopoulou. Symposium on Intelligent Data Analysis (IDA), page 145-157. Springer, (2015)
2 years ago by @ies
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active
budget
data
learning
not_ies
streams
tolerance
window
activebudgetdatalearningnot_iesstreamstolerancewindow
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