100% tevredenheidsgarantie Direct beschikbaar na je betaling Lees online óf als PDF Geen vaste maandelijkse kosten
logo-home
Eerder door jou gezocht
Eerder door jou gezocht
logo
Samenvatting Advanced Analytics in a Big Data World (D0S06B) €10,46
In winkelwagen
Snel navigeren naar
Voorbeeld
  2.  
  3. Katholieke Universiteit Leuven (KU Leuven)
  4.  
  5. Handelsingenieur
  6.  
  7. Advanced Analytics (D0S06B)

Samenvatting

Samenvatting Advanced Analytics in a Big Data World (D0S06B)
 0 keer verkocht

Samenvatting van de volledige cursus op basis van de notities en slides voor het vak Advanced Analytics in a Big Data World (D0S06B) HIR(B) 2e master. Geslaagd eerste zit.

Voorbeeld 4 van de 91  pagina's

Document informatie

  • 12 maart 2025
  • 91
  • 2023/2024
  • Samenvatting

Onderwerpen

Geschreven voor

Alle documenten voor dit vak (1)

Verkoper

avatar-seller
rikteugels

Ontvangen beoordelingen

Voorbeeld van de inhoud

ADVANCED ANALYTICS
Prof. Seppe vanden Broucke




KU Leuven

,TABLE OF CONTENTS
Table of Contents...................................................................................................................................1
1 Introduction........................................................................................................................................4
1.1 Setting the Scene.........................................................................................................................4
1.2 Components of Data Science.......................................................................................................4
1.3 Process, People, and Problems....................................................................................................5
2 Preprocessing and Feature Engineering..............................................................................................7
2.1 Preprocessing Steps.....................................................................................................................7
2.2 Feature Engineering...................................................................................................................10
2.3 Conclusion.................................................................................................................................10
3 Supervised Learning..........................................................................................................................12
3.1 (Logistic) Regression..................................................................................................................12
3.2 Decision and Regression Trees...................................................................................................13
3.3 K-NN...........................................................................................................................................15
4 Model Evaluation..............................................................................................................................16
4.1 Introduction...............................................................................................................................16
4.2 Classification Performance.........................................................................................................16
4.3 Regression Performance............................................................................................................19
4.4 Cross-Validation and Tuning......................................................................................................19
4.5 Additional Notes........................................................................................................................20
4.6 Monitoring and Maintenance....................................................................................................21
5 Ensemble Modelling: Bagging and Boosting.....................................................................................23
5.1 Introduction...............................................................................................................................23
5.2 Bagging......................................................................................................................................23
5.3 Boosting.....................................................................................................................................24
5.4 Comparing Bagging and Boosting..............................................................................................25
6 Interpretability..................................................................................................................................26
6.1 Introduction...............................................................................................................................26
6.2 Feature importance...................................................................................................................26
6.3 Partial Dependence Plots...........................................................................................................27
6.4 Individual Conditional Expectation plots....................................................................................27
6.5 LIME...........................................................................................................................................27
6.6 Shapley values...........................................................................................................................28
6.7 Conclusion.................................................................................................................................28


1

,7 Deep Learning Part 1: Foundations and Images................................................................................29
7.1 Introduction...............................................................................................................................29
7.2 Foundations of artificial neural networks..................................................................................30
7.3 Delving deeper into Artificial Neural Networks..........................................................................31
7.4 The convolutional architecture..................................................................................................33
7.5 Interpretation of convolutional neural networks.......................................................................35
7.6 Generative models for images...................................................................................................37
8 Unsupervised Learning.....................................................................................................................45
8.1 Frequent itemset and association rule mining...........................................................................45
8.2 Clustering...................................................................................................................................47
8.3 Dimensionality reduction...........................................................................................................50
8.4 Anomaly detection.....................................................................................................................51
9 Data Science Tools............................................................................................................................53
9.1 In-memory analytics..................................................................................................................53
9.2 Python and R..............................................................................................................................53
9.3 Visualization...............................................................................................................................53
9.4 The road to big data...................................................................................................................54
9.5 Notebooks and development environments.............................................................................54
9.6 Labeling......................................................................................................................................55
9.7 File formats................................................................................................................................55
9.8 Packaging and versioning systems.............................................................................................57
9.9 Model deployment....................................................................................................................58
10 Hadoop, Spark, and Streaming Analytics........................................................................................61
10.1 Introduction.............................................................................................................................61
10.2 Hadoop: HDFS and MapReduce...............................................................................................61
10.3 Spark: SparkSQL and MLlib......................................................................................................64
10.4 Streaming analytics and other trends......................................................................................67
11 Deep Learning Part 2: Text, Representation Learning and Recurrence...........................................69
11.1 Traditional approaches............................................................................................................69
11.2 Word embeddings and representational learning...................................................................70
11.3 Recurrent neural networks (RNN)............................................................................................73
11.4 From RNNs to Transformers....................................................................................................75
11.5 Conclusion...............................................................................................................................77
12 Graph Analytics...............................................................................................................................78
12.1 Graph construction.................................................................................................................78
12.2 Graph metrics..........................................................................................................................78

2

, 12.3 Community mining...................................................................................................................79
12.4 Making predictions: Relational learners..................................................................................80
12.5 Making predictions: Featurization...........................................................................................82
12.6 Example...................................................................................................................................82
12.7 A word on validation................................................................................................................82
12.8 Node2vec and deep learning...................................................................................................83
12.9 Tooling.....................................................................................................................................86
12.10 NoSQL....................................................................................................................................86
12.11 Graph databases....................................................................................................................87
13 Wrap Up..........................................................................................................................................89
13.1 Key pitfalls................................................................................................................................89
13.2 Closing......................................................................................................................................90




3

Dit zijn jouw voordelen als je samenvattingen koopt bij Stuvia:

Bewezen kwaliteit door reviews

Bewezen kwaliteit door reviews

Studenten hebben al meer dan 850.000 samenvattingen beoordeeld. Zo weet jij zeker dat je de beste keuze maakt!

In een paar klikken geregeld

In een paar klikken geregeld

Geen gedoe — betaal gewoon eenmalig met iDeal, Bancontact of creditcard en je bent klaar. Geen abonnement nodig.

Focus op de essentie

Focus op de essentie

Studenten maken samenvattingen voor studenten. Dat betekent: actuele inhoud waar jij écht wat aan hebt. Geen overbodige details!

Veelgestelde vragen

Wat krijg ik als ik dit document koop?

Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.

Tevredenheidsgarantie: hoe werkt dat?

Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.

Van wie koop ik deze samenvatting?

Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper rikteugels. Stuvia faciliteert de betaling aan de verkoper.

Zit ik meteen vast aan een abonnement?

Nee, je koopt alleen deze samenvatting voor €10,46. Je zit daarna nergens aan vast.

Is Stuvia te vertrouwen?

4,6 sterren op Google & Trustpilot (+1000 reviews)

Afgelopen 30 dagen zijn er 71250 samenvattingen verkocht

Opgericht in 2010, al 15 jaar dé plek om samenvattingen te kopen

Start met verkopen

Laatst bekeken door jou

Tentamen (uitwerkingen) ·

(0)

ATI TEAS 7 READING 47 QUESTIONS & ANSWERS: LATEST 2022/2023

Tentamen (uitwerkingen) ·

(0)

ATI Fundamentals Exam- Amara questions with correct answers

Tentamen (uitwerkingen) ·

(0)

NCCT Phlebotomy Certification/ 144 Q&A/ A+ Score Solution.
€10,46
  • (0)
In winkelwagen
Toegevoegd