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Summary Machine Learning for business
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- Universiteit Antwerpen (UA)
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Machine learningBUSINESS ENGINEERING – FINANCIAL
ENGINEERING
2024-2025
Data Science Challenge: 5
Exam: 15
- Multiple choice
➢ No guessing correction
➢ Censure: if 5 answers per question, you need to answer 60% correct to pass the multiple
choice part
➢ So answer all questions
➢ Details on the exam
- Open question
,Inhoudsopgave
LECTURE 1 – WHAT IS DATA SCIENCE AND MACHINE LEARNING? ....................................................................... 4
1 INTRODUCTION ............................................................................................................................................................................. 4
2 TERMINOLOGY.............................................................................................................................................................................. 4
2.1 Artificial Intelligence ........................................................................................................................................................... 5
2.2 Machine Learning ............................................................................................................................................................... 5
3 DATA SCIENTIST ........................................................................................................................................................................... 7
3.1 Importance ........................................................................................................................................................................... 7
3.2 Roles and skills .................................................................................................................................................................... 7
LECTURE 2 – MACHINE LEARNING .......................................................................................................................... 8
1 EXPLAINING VERSUS PREDICTING MODELLING ............................................................................................................................... 8
2 DATA PREPROCESSING ................................................................................................................................................................. 10
2.1 Sampling .............................................................................................................................................................................. 10
2.2 Encoding .............................................................................................................................................................................. 11
2.3 Missing values .................................................................................................................................................................... 12
2.4 Outliers ............................................................................................................................................................................... 12
2.5 Normalizing ........................................................................................................................................................................ 13
2.6 Discretization .................................................................................................................................................................... 14
3 SOME NOTES ABOUT CHATGPT ................................................................................................................................................... 14
LECTURE 3 – INTRODUCTION TO PREDICTIVE MODELING ............................................................................... 16
1. TERMINOLOGY ............................................................................................................................................................................ 16
2. FINDING INFORMATIVE VARIABLES FROM THE DATA ................................................................................................................... 17
3. DECISION TREES.......................................................................................................................................................................... 17
4. METHOLOGY OF DECISION TREES IN MORE DETAIL ..................................................................................................................... 18
5. OVERFITTING AND ITS AVOIDANCE ........................................................................................................................................... 20
5.1 Overfitting .......................................................................................................................................................................... 20
5.2 Avoidance ........................................................................................................................................................................... 21
5.3 Bias/ variance trade-off .....................................................................................................................................................22
LECTURE 4 – ASSESSING AND VISUALIZING MODEL PERFORMANCE ................................................................ 23
1. EVALUATING CLASSIFIERS ............................................................................................................................................................23
1.1 Accuracy .............................................................................................................................................................................. 23
1.2 Confusion matrix ................................................................................................................................................................ 23
1.3 Problems: unbalanced classes ............................................................................................................................................ 23
1.4 Problems: unequal costs and benefits .............................................................................................................................. 24
2. EXPECTED VALUE ....................................................................................................................................................................... 24
2.1 Expected Value for classifier evaluation .......................................................................................................................... 24
3. EVALUATION AND BASELINE PERFORMANCE .............................................................................................................................. 26
LECTURE 5 - LEVERAGING DATA SCIENCE: BUSINESS INSIGHTS, MODEL PERFORMANCE, AND EVIDENCE-
BASED DECISION MAKING .................................................................................................................................... 28
1 DECISION ANALYTIC THINKING I: WHAT IS A GOOD MODEL? (RECAP) ...................................................................................... 28
1.1 What is a Good Model? ...................................................................................................................................................... 28
1.2 Key concepts ...................................................................................................................................................................... 28
1.2.1 Positive and negatives ..................................................................................................................................................... 28
1.2.2 Accuracy .......................................................................................................................................................................... 28
1.2.3 The confusion matrix...................................................................................................................................................... 29
1.2.4 Cost- Benefit matrix ....................................................................................................................................................... 29
1.2.5 Expected Profit/ Value .................................................................................................................................................... 29
1.2.6 What is a good baseline?................................................................................................................................................ 29
2 VISUALIZING MODEL PERFORMANCE ........................................................................................................................................... 29
2.1 Ranking Classifier .............................................................................................................................................................. 30
2.2 Profit Curve........................................................................................................................................................................ 30
PAGINA 1
, 2.3 ROC Curve ......................................................................................................................................................................... 30
2.4 AUC (Area Under Curve)................................................................................................................................................... 31
2.5 Lift Curve (Cumulative Response Curve) ......................................................................................................................... 31
3 EVIDENCE AND PROBABILITIES.................................................................................................................................................... 34
3.1 Evidence .............................................................................................................................................................................. 34
3.2 Joint Probabilities and Independence............................................................................................................................... 34
3.3 Bayes Rule .......................................................................................................................................................................... 34
3.4 Naive Bayes and Conditional Independence ................................................................................................................... 34
3.5 Evidence Lift .......................................................................................................................................................................35
LECTURE 6 – SIMILARITY, NEIGHBORS, AND CLUSTERS .................................................................................... 37
1. SIMILARITY (BETWEEN INSTANCES) .............................................................................................................................................37
2. DISTANCE AND SIMILARITY MEASURES .......................................................................................................................................37
2.1 Euclidean distance ..............................................................................................................................................................37
2.2 Manhattan distance ...........................................................................................................................................................37
2.3 Cosine similarity ................................................................................................................................................................37
2.4 Jaccard similarity .............................................................................................................................................................. 38
2.5 Hamming distance ............................................................................................................................................................ 38
2.6 Levenshtein distance ........................................................................................................................................................ 38
3. K-NEAREST NEIGHBORS ............................................................................................................................................................. 38
4. HIERARCHICAL CLUSTERING (DENDROGRAMS) ......................................................................................................................... 40
5. K-MEANS CLUSTERING .............................................................................................................................................................. 40
LECTURE 7 – RECOMMENDER SYSTEMS ............................................................................................................... 41
1. WHAT IS A RECOMMENDER SYSTEM? ........................................................................................................................................... 41
2. PROBLEM DEFINITION: EVALUATING A RECOMMENDATION ALGORITHM .................................................................................. 41
3. RECOMMENDATION ALGORITHMS: TWO PERSPECTIVES ........................................................................................................... 42
3.1 Baselines, Content-Based, Collaborative Filtering and Hybrid Algorithms (Data perspective) .............................. 42
3.1.1 Baseline ............................................................................................................................................................................ 42
3.1.2 Content-based ................................................................................................................................................................. 42
3.1.3 Collaborative filtering ..................................................................................................................................................... 43
3.2 Pointwise, Pairwise and Listwise Learning-to-Rank (Learning perspective) ............................................................ 44
3.2.1 Pointwise: learning-to-rank ........................................................................................................................................... 44
3.2.2 Pairwise: learning-to-rank ............................................................................................................................................. 44
3.2.3 Listwise: learning-to-rank.............................................................................................................................................. 44
4. UNDER THE HOOD: BUILDING A PERSONALIZED RECOMMENDER SYSTEM ................................................................................ 44
LECTURE 8 – TEXT MINING .................................................................................................................................... 45
1. TEXT MINING APPLICATIONS ..................................................................................................................................................... 45
1.1 Unstructured vs. Structured Data ..................................................................................................................................... 45
1.2 Text Preprocessing............................................................................................................................................................. 45
1.3 Terminology: Documents, Tokens and Terms, Corpus ................................................................................................... 46
1.4 Bag of Words ..................................................................................................................................................................... 46
1.5 TF-IDF (Term Frequency - Inverse Document Frequency) ............................................................................................. 47
1.6 N-gram ............................................................................................................................................................................... 48
1.7 Named Entity Recognition ................................................................................................................................................ 48
1.8 Topic Model ....................................................................................................................................................................... 48
1.9 Word Embedding ............................................................................................................................................................... 49
2. ASSOCIATION RULE MINING...................................................................................................................................................... 49
2.1 Item sets ............................................................................................................................................................................. 49
2.2 Frequent Item sets............................................................................................................................................................. 49
2.3 Association Rules .............................................................................................................................................................. 50
2.4 Support .............................................................................................................................................................................. 50
2.5 Confidence ......................................................................................................................................................................... 50
2.6 Association Rule Mining: Apriori Algorithm .................................................................................................................. 50
2.7 Lift .......................................................................................................................................................................................52
PAGINA 2
, LECTURE 9 – NEURAL NETWORKS AND DEEP LEARNING ................................................................................... 53
1. NEURAL NETWORKS....................................................................................................................................................................53
1.1 The Perceptron ....................................................................................................................................................................53
1.2 Activation Function ............................................................................................................................................................53
1.3 Multi-Layer Perceptron ..................................................................................................................................................... 54
1.4 Forward Pass .......................................................................................................................................................................55
1.5 Loss Function ..................................................................................................................................................................... 56
1.6 Backpropagation (Backward Pass) .................................................................................................................................. 56
1.7 Gradient Descent Algorithm ............................................................................................................................................. 56
1.8 Stochastic Gradient Descent ............................................................................................................................................ 56
2. DEEP LEARNING......................................................................................................................................................................... 57
2.1 Convolutional Neural Networks (CNNs) ..........................................................................................................................57
2.2 Recurrent Neural Networks (RNNs) ................................................................................................................................ 58
2.3 Autoencoders ..................................................................................................................................................................... 58
2.4 Transformers ..................................................................................................................................................................... 59
2.5 Foundation Models (Large Language Models) ............................................................................................................... 59
LECTURE 10 – ENSEMBLE METHODS SVM ............................................................................................................. 61
1. ENSEMBLE METHODS ................................................................................................................................................................... 61
1.1 Combine by consensus ........................................................................................................................................................ 61
1.1.1 Bagging .............................................................................................................................................................................. 61
1.1.2 Random Forests ............................................................................................................................................................... 62
1.2 Combine by learning .......................................................................................................................................................... 62
1.2.1 Boosting ........................................................................................................................................................................... 62
1.2.2 Stacking ........................................................................................................................................................................... 63
2. A BRIEF INTRO TO THE SUPPORT VECTOR MACHINE ................................................................................................................. 64
3. DATA SCIENCE ETHICS ............................................................................................................................................................... 66
3.1 Data gathering: Privacy, A/B Testing and Bias ............................................................................................................... 67
3.1.1 Privacy .............................................................................................................................................................................. 67
3.1.2 Experimentation ............................................................................................................................................................. 67
3.1.3 Bias................................................................................................................................................................................... 68
3.2 Data preprocessing: Proxies, Government Backdoors ................................................................................................... 68
3.3 Modeling: ZK Proofs, Discrimination .............................................................................................................................. 69
3.4 Model evaluation: explain ................................................................................................................................................ 69
3.5 Deployment: Unintended consequences .......................................................................................................................... 70
PAGINA 3
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