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Class Notes for CSE30246 at University of Notre Dame (ND)

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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 4: Association Rule Learning, Unsupervised Learning, Dependent And Independent Variables

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 9: Confusion Matrix

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 5: Euclidean Distance

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 2: Signal-To-Noise Ratio, Data Quality, Data Science

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture 9: Lectures 9 and 10 Notes Part 4

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 9: Hyperparameter Optimization

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 9: Odds Ratio, Receiver Operating Characteristic

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 7: Logistic Regression, Generalized Linear Model, Logit

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 5: Unsupervised Learning

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 7: Lincoln Near-Earth Asteroid Research, Logistic Function, Maximum Likelihood Estimation

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6 Jan 2019
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 9: Random Forest, Adaboost, Machine Learning

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6 Jan 2019
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Lectures 9 and 10 notes: meta-learning, combination function , how do i bring predictions together in order to learn, approach in which you use another
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NDCSE30246Fred NwwangangaSpring

CSE30246 Lecture Notes - Lecture 2: Unsupervised Learning, Central Tendency, Data Science

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6 Jan 2019
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