Machine Learning for Characterizing Climate-related Disasters (Student Presentation, Group 20)

Sebastian Raschka · Advanced ·📐 ML Fundamentals ·5y ago
This is a STAT 451 class project presentation by Eliot Kim, Jonathan Lala, and Noah Simandl This presentation is shared with the students' permission. Abstract: Predictions of disaster impact are critical in mitigating human and material losses. Accurate predictions are especially necessary in the coming years due to global warming, which has increased the severity of natural disasters. Ma- chine learning algorithms provide a powerful tool to model the complex relationships among the natural, social, and economic variables which dictate the impact of disasters. The aim of this project is t…
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1 Sebastian Raschka - SIteInterlock
Sebastian Raschka - SIteInterlock
Sebastian Raschka
 2 Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]
Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]
Sebastian Raschka
 3 Intro to Deep Learning -- L09 Regularization [Stat453, SS20]
Intro to Deep Learning -- L09 Regularization [Stat453, SS20]
Sebastian Raschka
 4 Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]
Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]
Sebastian Raschka
 5 Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]
Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]
Sebastian Raschka
 6 Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]
Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]
Sebastian Raschka
 7 Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]
Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]
Sebastian Raschka
 8 Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]
Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]
Sebastian Raschka
 9 Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]
Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]
Sebastian Raschka
 10 Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
Sebastian Raschka
 11 Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
Sebastian Raschka
 12 Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]
Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]
Sebastian Raschka
 13 Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]
Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]
Sebastian Raschka
 14 1.2 What is Machine Learning (L01: What is Machine Learning)
1.2 What is Machine Learning (L01: What is Machine Learning)
Sebastian Raschka
 15 1.3 Categories of Machine Learning (L01: What is Machine Learning)
1.3 Categories of Machine Learning (L01: What is Machine Learning)
Sebastian Raschka
 16 1.4 Notation (L01: What is Machine Learning)
1.4 Notation (L01: What is Machine Learning)
Sebastian Raschka
 17 1.1 Course overview (L01: What is Machine Learning)
1.1 Course overview (L01: What is Machine Learning)
Sebastian Raschka
 18 1.5 ML application (L01: What is Machine Learning)
1.5 ML application (L01: What is Machine Learning)
Sebastian Raschka
 19 1.6 ML motivation (L01: What is Machine Learning)
1.6 ML motivation (L01: What is Machine Learning)
Sebastian Raschka
 20 2.1 Introduction to NN (L02: Nearest Neighbor Methods)
2.1 Introduction to NN (L02: Nearest Neighbor Methods)
Sebastian Raschka
 21 2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
Sebastian Raschka
 22 2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
Sebastian Raschka
 23 2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
Sebastian Raschka
 24 2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
Sebastian Raschka
 25 2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
Sebastian Raschka
 26 3.1 (Optional) Python overview
3.1 (Optional) Python overview
Sebastian Raschka
 27 3.2 (Optional) Python setup
3.2 (Optional) Python setup
Sebastian Raschka
 28 3.3 (Optional) Running Python code
3.3 (Optional) Running Python code
Sebastian Raschka
 29 4.1 Intro to NumPy (L04: Scientific Computing in Python)
4.1 Intro to NumPy (L04: Scientific Computing in Python)
Sebastian Raschka
 30 4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
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 31 4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
Sebastian Raschka
 32 4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
Sebastian Raschka
 33 4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)
4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)
Sebastian Raschka
 34 4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
Sebastian Raschka
 35 4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
Sebastian Raschka
 36 4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)
4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)
Sebastian Raschka
 37 4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
Sebastian Raschka
 38 4.10 Matplotlib (L04: Scientific Computing in Python)
4.10 Matplotlib (L04: Scientific Computing in Python)
Sebastian Raschka
 39 5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
Sebastian Raschka
 40 5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
Sebastian Raschka
 41 5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
Sebastian Raschka
 42 5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
Sebastian Raschka
 43 5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
Sebastian Raschka
 44 5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)
5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)
Sebastian Raschka
 45 6.1 Intro to Decision Trees (L06: Decision Trees)
6.1 Intro to Decision Trees (L06: Decision Trees)
Sebastian Raschka
 46 6.2 Recursive algorithms & Big-O (L06: Decision Trees)
6.2 Recursive algorithms & Big-O (L06: Decision Trees)
Sebastian Raschka
 47 6.3 Types of decision trees (L06: Decision Trees)
6.3 Types of decision trees (L06: Decision Trees)
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 48 6.4 Splitting criteria (L06: Decision Trees)
6.4 Splitting criteria (L06: Decision Trees)
Sebastian Raschka
 49 About the Midterm exam
About the Midterm exam
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 50 6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
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 51 6.6 Improvements & dealing with overfitting (L06: Decision Trees)
6.6 Improvements & dealing with overfitting (L06: Decision Trees)
Sebastian Raschka
 52 6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)
6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)
Sebastian Raschka
 53 7.1 Intro to ensemble methods (L07: Ensemble Methods)
7.1 Intro to ensemble methods (L07: Ensemble Methods)
Sebastian Raschka
 54 7.2 Majority Voting (L07: Ensemble Methods)
7.2 Majority Voting (L07: Ensemble Methods)
Sebastian Raschka
 55 7.3 Bagging (L07: Ensemble Methods)
7.3 Bagging (L07: Ensemble Methods)
Sebastian Raschka
 56 7.4 Boosting and AdaBoost (L07: Ensemble Methods)
7.4 Boosting and AdaBoost (L07: Ensemble Methods)
Sebastian Raschka
 57 7.5 Gradient Boosting (L07: Ensemble Methods)
7.5 Gradient Boosting (L07: Ensemble Methods)
Sebastian Raschka
 58 7.6 Random Forests (L07: Ensemble Methods)
7.6 Random Forests (L07: Ensemble Methods)
Sebastian Raschka
 59 7.7 Stacking (L07: Ensemble Methods)
7.7 Stacking (L07: Ensemble Methods)
Sebastian Raschka
 60 8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)
8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)
Sebastian Raschka
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