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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)
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  • Vak
  • Advanced Analytics (D0S06B)
  • Instelling
  • Katholieke Universiteit Leuven (KU Leuven)

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

  • advanced analytics
  • big data
  • hadoop
  • spark
  • streaming data
  • predictive models
  • machine learning
  • deep learning
  • text mining
  • ai

Geschreven voor

  • Katholieke Universiteit Leuven (KU Leuven)
  • Handelsingenieur
  • Advanced Analytics (D0S06B)
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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




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