Machine Learning Overview

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Questions and Answers

Which of the following best describes a key ethical concern regarding the use of AI in determining sexual orientation, as highlighted by Wang & Kosinski?

The foremost concern is the challenge in ensuring that AI models are easily interpretable by the audience.

The use of AI in this context can lead to privacy violations, reinforcement of stereotypes, and weak inferences due to insufficient statistical evidence. (correct)

The primary issue is the efficient processing of data, which leads to computational errors.

The main ethical dilemma lies in the potential misuse of AI by malicious actors to cause environmental damage.

What is a significant risk associated with Large Language Models (LLMs)?

The limitations in adapting to complex climate simulation and modelling.

The potential for discrimination, spread of misinformation, privacy leaks, and environmental damage. (correct)

The challenges in gathering representative training data when used in building design.

The lack of explainability when used in transport and fuel efficiency applications.

Which of the following is NOT described as a direct application of AI in addressing climate change?

Analyzing historical medical records for disease patterns. (correct)

Supporting energy-efficient designs in buildings.

Improving logistics and fuel efficiency in transport.

Optimizing supply and demand in electricity networks.

Why is the use of 'interpretable models' important in the context of Explainable AI (XAI)?

Because they can help create a balance between explanatory power and model performance, while also enabling domain-specific needs to be considered. (D) Signup and view all the answers

What does the text suggest is a key consideration when using XAI in policy making?

The necessity of tailoring XAI explanations to suit a specific audience's level of understanding. (D) Signup and view all the answers

What does the term 'black-box models' refer to in the context of AI?

Models with such complex logic that their decision-making processes are not easily understood. (C) Signup and view all the answers

Which of the following are mentioned as a potential method to mitigate privacy risks?

Applying privacy-protecting techniques such as differential privacy. (C) Signup and view all the answers

What is considered a necessary next step in integrating XAI methodologies?

The further integration into socio-technical systems to enhance responsible use of AI. (A) Signup and view all the answers

What is the primary focus of Machine Learning compared to statistical models?

Learning input-output relationships (C) Signup and view all the answers

Which of the following statements about Machine Learning is true?

It can work with unstructured data. (A) Signup and view all the answers

Which of the following methods is NOT typically considered a popular Machine Learning method?

Central Limit Theorem (C) Signup and view all the answers

In which scenario would Unsupervised Learning be applied?

When exploring relationships in data without labels (B) Signup and view all the answers

What limitation does Machine Learning face compared to traditional statistical methods?

Often no insight into causality (A) Signup and view all the answers

Which of the following is characteristic of Reinforcement Learning?

It learns through providing feedback based on rewards. (A) Signup and view all the answers

Why has Machine Learning become increasingly popular in recent years?

Increase in large datasets and powerful computing! (C) Signup and view all the answers

What is a major difference between Supervised Learning and Unsupervised Learning?

Supervised Learning works with labeled data. (C) Signup and view all the answers

What is the main role of the root node in a Decision Tree?

It contains the entire data set. (D) Signup and view all the answers

Which method is NOT used to avoid overfitting in Decision Trees?

Increasing the number of splits indefinitely. (B) Signup and view all the answers

What does Information Gain in a Decision Tree indicate?

Which split is the most informative. (B) Signup and view all the answers

Why might feature importance in Decision Trees be considered unstable?

It can change with different training datasets. (A) Signup and view all the answers

How is precision defined in the context of Decision Tree model performance metrics?

Correct positive predictions divided by all positive predictions. (D) Signup and view all the answers

Which characteristic primarily defines a split node in a Decision Tree?

It decides based on characteristics of features. (B) Signup and view all the answers

What is the purpose of Matthew’s Correlation Coefficient (MCC) in model evaluation?

To combine multiple evaluation metrics into a single score. (C) Signup and view all the answers

What does the concept of 'greedy algorithm' signify in the context of Decision Trees?

Selecting the first available split that seems optimal. (C) Signup and view all the answers

What is one of the main advantages of using ensemble models?

They increase generalization by combining diverse sources of information. (C) Signup and view all the answers

Which hyperparameter in Random Forests controls the maximum depth of trees?

max_depth (C) Signup and view all the answers

What technique does Random Forest use to enhance diversity among trees?

Bagging and random patching (A) Signup and view all the answers

How does boosting improve model performance?

By sequentially training models to focus on previous errors. (C) Signup and view all the answers

What is a key disadvantage of using Random Forest models compared to individual decision trees?

They are more computationally intensive to run. (C) Signup and view all the answers

Why might hyperparameter tuning be necessary when training Neural Networks?

To optimize performance across various datasets. (C) Signup and view all the answers

Which of the following statements accurately describes the function of training models on different folds?

It provides an average performance measure to optimize hyperparameters. (B) Signup and view all the answers

What is the primary characteristic of ensemble models?

They combine multiple weak models to enhance overall model strength. (B) Signup and view all the answers

What is a significant advantage of using LIME for explaining predictions?

It works with any model type. (D) Signup and view all the answers

What is a key advantage of Gradient Boosted Trees (GBTs) over single decision trees?

They are better at learning complex non-linear relationships. (A) Signup and view all the answers

What is a key feature of counterfactual explanations?

They modify the original data point minimally. (C) Signup and view all the answers

Which of the following best describes the purpose of Partial Dependence Plots (PDPs)?

To show the effect of a target feature on the predicted outcome. (C) Signup and view all the answers

Which hyperparameter in Gradient Boosted Trees determines the number of trees to be constructed?

n_estimators (C) Signup and view all the answers

What is a disadvantage of using Individual Conditional Expectation (ICE) plots?

They may misrepresent average predictions across groups. (A) Signup and view all the answers

What distinguishes boosting methods from Random Forests in terms of model training?

Boosting models learn from the residuals of previous models. (C) Signup and view all the answers

What criticism has been leveled against the COMPAS algorithm?

It discriminates against specific groups in risk assessment. (C) Signup and view all the answers

What makes ensembles like Random Forests more robust compared to single models?

They are less prone to overfitting. (D) Signup and view all the answers

Which property of embeddings ensures that relationships between data points are maintained?

Semantic preservation (A) Signup and view all the answers

Why is explainable AI considered essential in machine learning?

It enhances trust and ethical use of algorithms. (B) Signup and view all the answers

What is a primary challenge associated with LIME when interpreting results?

It ignores correlations between features. (A) Signup and view all the answers

In the context of embeddings, what is one way to create them unsupervised?

Utilizing a bottleneck of an autoencoder. (A) Signup and view all the answers

In the context of explainable AI, what is the purpose of using anchors?

To provide high precision explanations for specific model predictions. (C) Signup and view all the answers

What condition must be met for a relationship to be considered a causal one?

The cause must precede the effect in time. (D) Signup and view all the answers

What is a general disadvantage of ensemble methods like boosting and Random Forests?

They increase overall training complexity. (B) Signup and view all the answers

Study Notes

Machine Learning Lecture Summaries

Machine Learning (ML) is a field that enables computers to learn without explicit programming. Arthur Samuel defined it in 1959.
Applications of ML include email spam filters, chatbots, fraud detection, recommendation systems, and targeted advertisements.
The increasing availability of large datasets and powerful computing resources contributes to ML's popularity.
ML can process unstructured data types like images, videos, text, and audio unlike statistical models.
Statistical models infer relationships, focusing on "why" and relying on established theories (like the law of large numbers).
Parameters in statistical models are explainable.
ML models focus on prediction, learning input-output relationships based on data, and performance without focusing on causality.
Models like Linear Regression, Logistic Regression, Decision Trees, Random Forests, Artificial Neural Networks, Gradient Boosting, Clustering (K-means, DBSCAN), and Bayesian Networks are popular ML methods.
The process of building an ML model involves iterative steps including data study and cleaning, feature discovery, correlation exploration, basic model training, and performance evaluation using metrics like R-squared, MAE, and RMSE.
Overfitting occurs when a model fits the training data too closely, whereas underfitting describes a model that's too simple to capture the data's patterns. The bias-variance trade-off represents the balancing act between these errors.
Techniques like splitting the data into training and testing sets, pre-pruning (which limits model complexity), and post-pruning (which removes non-significant branches) are used to prevent overfitting and improve the model's generalization ability.
Feature importance in decision trees is evaluated based on the reduction of node impurity. This measure may vary with the specific model used.
Model evaluation is often through confusion matrices and metrics such as accuracy, precision, recall, specificity, and Matthew's correlation coefficient.
Artificial Neural Networks (ANNs) are powerful tools for non-linear relationships, scaling well with data, but they can be less interpretable ("black box"). ANNs use layers of interconnected nodes with weighted connections to learn patterns from data.
Training ANNs often involves iterative processes like backpropagation to minimize the error between predicted and actual values. Loss functions like MSE (Mean Squared Error) and Cross-entropy are used for this.
Hyperparameters like the number of hidden layers, batch size, learning rate, and regularization parameters require careful tuning to optimize performance.
K-Fold Cross-Validation can improve the robustness of model evaluation by dividing data into folds, and training/testing on different subsets, resulting in more reliable model evaluations.
Ensemble models, like Random Forests and Gradient Boosted Trees, combine multiple simpler models to improve prediction accuracy and reduce overfitting when a single model is insufficient. They achieve improved generalization and handling of complex relationships.
Embeddings convert discrete data (like words, images) into continuous vector representations, enhancing their usability in various machine learning tasks. This allows algorithms to extract meaningful information from both structured and unstructured data.
Relationships between data can be represented and quantified using semantic preservation metrics like Euclidean distance or cosine similarity in data.
Causality in machine learning is crucial for policy analysis. The crucial conditions for establishing causality include association, temporal precedence, and absence of other confounding factors.
Causal models offer better performance for out-of-distribution predictions (i.e., when the model handles data beyond that it was trained on).
Explainable AI (XAI) techniques aim to make the predictions of machine learning models easy to understand and trust. These techniques use methods like PDPs (partial dependence plots), ICE (Individual Conditional Expectation), and SHAP values to provide insight into how models make predictions and the features they use in these predictions.
Considerations for explaining the behaviour of an ML model include transparency, accuracy, fidelity, consistency, comprehensibility, and stability, to give users valuable insights and to ensure responsible and ethical deployments of AI models.

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Description

Explore the fundamentals of Machine Learning, including its definition, key applications, and how it differs from traditional statistical models. This summary covers various ML models and their capabilities in processing diverse data types. Perfect for anyone interested in understanding the basics of ML technology.