Podcast
Questions and Answers
What is the purpose of data transformation in machine learning processing steps?
What is the purpose of data transformation in machine learning processing steps?
Which of the following methods is NOT part of the decision-making process in machine learning?
Which of the following methods is NOT part of the decision-making process in machine learning?
How can the quality of a model be validated according to evaluation steps?
How can the quality of a model be validated according to evaluation steps?
Which metric is used to gauge the correctness of a classification model?
Which metric is used to gauge the correctness of a classification model?
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What does a confusion matrix allow you to evaluate in a classification system?
What does a confusion matrix allow you to evaluate in a classification system?
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When measuring an error in predictions, which of the following is an example of calculating error?
When measuring an error in predictions, which of the following is an example of calculating error?
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What is not included in the steps of ML processing as outlined in the content?
What is not included in the steps of ML processing as outlined in the content?
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In the context of machine learning, what does 'clustering' refer to?
In the context of machine learning, what does 'clustering' refer to?
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What is the primary aim of machine learning?
What is the primary aim of machine learning?
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Which of the following steps is NOT typically part of the machine learning processing steps?
Which of the following steps is NOT typically part of the machine learning processing steps?
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Which aspect of algorithms is most relevant to machine learning?
Which aspect of algorithms is most relevant to machine learning?
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What distinguishes machine learning from traditional programming?
What distinguishes machine learning from traditional programming?
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What type of computing is primarily involved in machine learning?
What type of computing is primarily involved in machine learning?
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What is one of the key features of machine learning algorithms?
What is one of the key features of machine learning algorithms?
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Why are traditional programming methods limited compared to machine learning?
Why are traditional programming methods limited compared to machine learning?
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Which of the following statements is true regarding machine learning algorithms?
Which of the following statements is true regarding machine learning algorithms?
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What is the first step in the ML processing steps?
What is the first step in the ML processing steps?
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What does 'TP' stand for in the context of binary classification?
What does 'TP' stand for in the context of binary classification?
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Which step involves cleaning and preparing data for analysis?
Which step involves cleaning and preparing data for analysis?
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What is the definition of True Negatives (TN)?
What is the definition of True Negatives (TN)?
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What is the purpose of the modelling step in ML processing?
What is the purpose of the modelling step in ML processing?
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Which equation calculates Sensitivity (SE)?
Which equation calculates Sensitivity (SE)?
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What follows after the modelling step in the ML processing steps?
What follows after the modelling step in the ML processing steps?
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What does a False Positive (FP) indicate?
What does a False Positive (FP) indicate?
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During which step are complex objects mapped in ML processing?
During which step are complex objects mapped in ML processing?
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What does 'SP' represent in binary classification?
What does 'SP' represent in binary classification?
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Which of the following is NOT a step in the ML processing?
Which of the following is NOT a step in the ML processing?
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Which of the following is NOT a key metric used in assessing classifier performance?
Which of the following is NOT a key metric used in assessing classifier performance?
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What is the final step in the ML processing steps?
What is the final step in the ML processing steps?
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What is the relationship between True Positives (TP) and False Negatives (FN) in calculating Sensitivity?
What is the relationship between True Positives (TP) and False Negatives (FN) in calculating Sensitivity?
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Which option best describes the goal of evaluating and interpretation in ML?
Which option best describes the goal of evaluating and interpretation in ML?
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What percentage does Specificity (SP) indicate?
What percentage does Specificity (SP) indicate?
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What does the coefficient of determination, $R^2$, represent in regression analysis?
What does the coefficient of determination, $R^2$, represent in regression analysis?
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Which formula correctly represents the sum of squared errors (SSE)?
Which formula correctly represents the sum of squared errors (SSE)?
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What does $SS_{res}$ represent in regression analysis?
What does $SS_{res}$ represent in regression analysis?
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In the formula for the coefficient of determination, what does $SS_{tot}$ signify?
In the formula for the coefficient of determination, what does $SS_{tot}$ signify?
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How is the $R^2$ value interpreted when it equals 1?
How is the $R^2$ value interpreted when it equals 1?
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What does a higher sum of squared errors (SSE) indicate about a regression model?
What does a higher sum of squared errors (SSE) indicate about a regression model?
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What does the error term represent in the regression equation?
What does the error term represent in the regression equation?
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What aspect do the steps in machine learning primarily focus on?
What aspect do the steps in machine learning primarily focus on?
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What is the primary reason for characterizing the ECG using specific parameters like ST segment deviation?
What is the primary reason for characterizing the ECG using specific parameters like ST segment deviation?
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Which step in the ML processing involves cleaning and preparing data for analysis?
Which step in the ML processing involves cleaning and preparing data for analysis?
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Why is it not advisable to use BMI, weight, and height simultaneously as features in data transformation?
Why is it not advisable to use BMI, weight, and height simultaneously as features in data transformation?
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What is the final step involved in the ML processing steps as outlined?
What is the final step involved in the ML processing steps as outlined?
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Which of the following is NOT included in the modelling phase of ML processing?
Which of the following is NOT included in the modelling phase of ML processing?
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What can ST segment deviation indicate in an ECG assessment?
What can ST segment deviation indicate in an ECG assessment?
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In the context of data selection, what is the primary objective?
In the context of data selection, what is the primary objective?
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Which data processing step follows data selection in the ML processing steps?
Which data processing step follows data selection in the ML processing steps?
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Study Notes
Introduction to Machine Learning
- Machine Learning (ML) is a branch of AI/computer science aiming to develop algorithms capable of learning and improving from experience.
- These algorithms analyze and interpret data, identifying patterns, making predictions, and making decisions based on training information.
Note
- These slides summarize class topics.
- They do not provide all necessary details.
- They are not the only study material.
Contents
- Introduction
- Machine learning processing steps
- Example
- Learning process
- Hard and soft computing
- Machine Learning evolution
- Conclusion
- Bibliography
1 | Introduction
- Machine learning (ML) is a branch of AI/computer science.
- ML aims to develop algorithms that learn and improve from experience.
- ML algorithms analyze data to identify patterns, make predictions, and make decisions.
- These algorithms improve their performance over time.
1 | Introduction
- ML aims to solve problems without explicit programming for every task.
- Data analysis and pattern recognition are central to ML.
1 | Introduction (continued)
- ML algorithms focus on improving performance metrics such as sensitivity, specificity, and accuracy.
- ML algorithms need training data to learn and make predictions or decisions.
1 | Introduction - ML vs. AI vs. DL
- AI: Mimicking human behavior with symbolic and data-based techniques
- ML: Subset of AI, using data to improve with experience
- DL: Subset of ML, enabling multi-layer neural networks
1 | Introduction - ML roots/applications
- ML has roots in AI and computer science, focusing on data-driven learning and improving performance from data.
- Focuses on improving the performance of learning methods.
- ML can process massive amounts of data in a variety of contexts.
1 | Introduction - ML models/techniques
- Clustering
- Neural Networks
- Decision Trees
- Fuzzy Systems
- Deep learning methods
1 | Introduction - Statistics vs. Machine Learning
- Statistics: Based on mathematics, focuses on inference from sample sets, and hypothesis testing
- Machine Learning: Based on AI, focuses on optimization problems, such as improving the performance of learning.
- Statistics has more theoretical foundations, while ML is often more practical and heuristic.
1 | Introduction - ML data types
- Static data: Values independent of each other (e.g., height/weight)
- Temporal data: Data sequences dependent on past values (e.g., time series).
1 | Introduction - ML problems
- Describing data
- Classifying data
- Regression
- Time-series data
- Predict future values
2 | ML processing steps
- Data selection
- Preprocessing
- Data transform
- Modelling
- Evaluation
2 | ML processing steps (Data selection)
- Available data?
- Subset selection (privacy concerns?)
- Relevant characteristics/features?
2 | ML processing steps (Data types)
- Binary data (yes/no)
- Categorical data (nominal/ordinal)
- Continuous data (real values)
2 | ML processing steps (Data types – cont.)
- Static Data
- Temporal Data
2 | ML processing steps (Preprocessing)
- Data cleaning/preparation, crucial for quality analysis
- Address missing values and abnormal values (outliers)
- Noise reduction
2 | ML processing steps (Preprocessing - Outliers)
- Outliers: Values deviating from typical data.
- Examples: telemonitoring data, sales revenue data (and others).
2 | ML processing steps (Preprocessing - Noise)
- High-frequency and low-frequency noise can degrade data.
- Example: ECG data, with noisy signals.
2 | ML processing steps (Preprocessing - Missing Values)
- Missing data in datasets is common.
- Missing values should be replaced or removed to train models.
2 | ML processing steps (Data transform)
- Feature Extraction: Summarizing complex objects into simpler features.
- Feature engineering: Creating new features/attributes from existing ones.
2 | ML processing steps (Data transform – cont.)
- Data reduction: Reduction of higher dimension data
- Data redundancy elimination
2 | ML processing steps (Data transform – cont.2)
- Example: time series data
- Frequency and period are sufficient representations of a sine wave.
2 | ML processing steps (Data transform – cont.3)
- Example: ECG data
- ST deviation and PR interval are good parameters
2 | ML processing steps - Data transform (Redundancy)
- Identifying/removing redundant attributes
- Example: Using height, weight, BMI. BMI is a function of the other two.
2 | ML processing steps - Modelling
- Building models that solve specific problems via available features/attributes.
2 | ML processing steps - Modelling (cont'd)
- Models types: Linear / Non-Linear, Supervised / Unsupervised, Clustering / Classification / Regression / Prediction
2 | ML processing steps - Modelling (cont'd.2)
- Linear Models
- Non-Linear Models (example: Neural networks)
2 | ML processing steps - Evaluation/Validation
- Model quality assessment methods
- Methods to check model reliability: Sensitivity / Specificity
2 | ML processing steps - Evaluation/Validation (cont'd)
- Error calculation example: Prediction error to determine model fitness.
- Accuracy metrics: Accuracy, Precision, Recall, F1-Score
- SSE, R-squared values in regression.
3 | Example: Diagnosing Diabetes
- Classification problem (binary)
- Target outcomes (diabetes/no diabetes)
- Data selection: Clinical patient data (age, weight, gender..)
- Data transformation: summarizing data, computing statistical information (mean, standard deviation, etc).
3 | Example: Diagnosing Diabetes (cont'd)
- Preprocessing: Handling missing values, outliers.
- Input features: Summarized statistical information
- Output predictions (Yes/ No Diabetes)
- Model training: Building a model.
4 | Learning Process
- Inductive Learning Hypothesis: Models well on training data -> likely to generalize to new data.
- Generalization: Models ability to perform on unseen data.
- Evaluation: Assessing quality
4 | Learning Process - Evaluation
- Hold-out validation: Splitting the data into training and testing sets to evaluate generalizability.
- Cross-validation (k-fold): Repeated hold-out validation improving robustness.
4 | Learning Process - Model Structure & Learning Process
- Model's structure (e.g., parameters) are assumed for the process
- Learning algorithms infer or compute a set of parameters from the training dataset.
4 | Learning Process - Model Structure & Learning Process (continued)
- Ex. Autoregressive model: Compute coefficients for time series prediction.
- Ex. Neural network: Compute neuronal network weights and biases.
4 | Learning Process - Data Characteristics
- Data balance: Training data should include representative instances for all classes (positive and negative examples).
4 | Learning Process - Data Characteristics (continued)
- Representative training data: Includes all possible combinations of input attributes from which new data instances may appear
5 | Hard and Soft Computing
- Hard computing: Based on mathematical and exact equations
- Soft computing: Tolerates imprecision and uncertainty in real-world applications
- Example: Automatic piloting for cars
5 | Hard and Soft Computing (continued)
- Biological inspiration: Artificial neurons inspired by biological neurons
- Fuzzy logic is an example of soft computing (fuzzy brain). Neural Network model inspiration as well
6 | Machine Learning Evolution
- Overview of the historical development of AI and Machine learning, including notable models/algorithms from various epochs
- Different phases of AI development (First/Second/Third Golden Ages)
- Key breakthroughs such as the neural network, Linear/non-linear Models etc
6 | Machine Learning Evolution (continued)
- Connectionist models (e.g., neural networks) and their ability to approximate non-linear mappings.
- Recurrent Neural Networks(RNNs), long short-term memory (LSTM).
6 | Machine Learning Evolution (continued, Generative AI)
- Generative AI: Creating new content based on learned patterns from large datasets
- Large Language Models (LLMs) and transformers
- Transformer models: Processing correlated word sequences without the need for recurrent structures.
- GPT-3, GPT-4
7 | Conclusion
- ML has various algorithms (no single best for each problem).
- Success in ML relies on experience, trial and error, and scientific principles
- Generalization capability is crucial
- ML is based on data and mathematical principles.
Bibliography
- A list of referenced sources, including books and webpages, are cited for further reading.
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