Probability and Statistics in Machine Learning

<p>To provide the model with labeled data for learning. (A)</p> Signup and view all the answers

<p>By transforming raw audio signals into a meaningful representation. (D)</p> Signup and view all the answers

<p>The model only learns from the training data. (B)</p> Signup and view all the answers

<p>Breaking the audio into frames. (A)</p> Signup and view all the answers

<p>To predict precise numerical values. (B)</p> Signup and view all the answers

<p>A full training cycle through the dataset. (B)</p> Signup and view all the answers

<p>They help improve the model's understanding of patterns. (C)</p> Signup and view all the answers

<p>It can cause overfitting by memorizing training data. (C)</p> Signup and view all the answers

<p>Regression focuses on numeric predictions while classification deals with categories. (C)</p> Signup and view all the answers

<p>It mimics the human brain through interconnected nodes. (D)</p> Signup and view all the answers

<p>It may need to be split into smaller batches for management. (C)</p> Signup and view all the answers

<p>It consists of multiple layers that extract complex patterns. (A)</p> Signup and view all the answers

<p>Identifying objects in images. (A)</p> Signup and view all the answers

<p>Large datasets. (C)</p> Signup and view all the answers

<p>Loss Ratio (LR) (A)</p> Signup and view all the answers

<p>High computational power such as GPUs. (C)</p> Signup and view all the answers

<p>They are always complex with multiple layers. (D)</p> Signup and view all the answers

<p>Incorrect predictions where a negative instance is predicted as positive. (B)</p> Signup and view all the answers

<p>Machine learning algorithm (A)</p> Signup and view all the answers

<p>Training or retraining (D)</p> Signup and view all the answers

<p>False (C)</p> Signup and view all the answers

<p>Reduced network latency (C)</p> Signup and view all the answers

<p>Processes data locally (C)</p> Signup and view all the answers

<p>Real-time monitoring (B)</p> Signup and view all the answers

<p>Limited ability to learn from data (C)</p> Signup and view all the answers

<p>One complete pass through the entire dataset (B)</p> Signup and view all the answers

<p>Continuing may lead to overfitting (C)</p> Signup and view all the answers

<p>Used to tune the model during the training (A)</p> Signup and view all the answers

<p>Regularization and dropout (C)</p> Signup and view all the answers

<p>A technique for summarizing audio characteristics relevant to speech (A)</p> Signup and view all the answers

<p>Specific errors made in classification tasks (D)</p> Signup and view all the answers

<p>To make predictions using trained models on new data (A)</p> Signup and view all the answers

<p>Training data is used for training, validation data is for tuning, and test data is for evaluation (A)</p> Signup and view all the answers

<p>A balance between precision and recall (B)</p> Signup and view all the answers

<p>To measure the difference between predicted probabilities and true probabilities (D)</p> Signup and view all the answers

<p>It involves taking steps in the direction of the steepest descent (C)</p> Signup and view all the answers

<p>To ensure optimal convergence during the training process (B)</p> Signup and view all the answers

<p>Multiple inputs from various sources (C)</p> Signup and view all the answers

<p>Continuous values based on inputs (A)</p> Signup and view all the answers

<p>Once the validation error starts to increase (B)</p> Signup and view all the answers

<p>The model may overshoot the optimal solution (D)</p> Signup and view all the answers

<p>It is not labeled with emotions (A)</p> Signup and view all the answers

<p>Random Forest handles small datasets better due to lower computational requirements (A)</p> Signup and view all the answers

<p>Randomly deactivates neurons during training to prevent overfitting (B)</p> Signup and view all the answers

<p>Random Forests use decision trees, while Neural Networks use interconnected neurons (A)</p> Signup and view all the answers

<p>It shows the performance for each class separately (B)</p> Signup and view all the answers

<p>Scaling the model to include new emotions without retraining (C)</p> Signup and view all the answers

<p>Bayesian Optimization is computationally faster and smarter than GridSearchCV (B)</p> Signup and view all the answers

<p>They are prone to overfitting on small datasets. (B)</p> Signup and view all the answers

<p>It is robust against overfitting by averaging tree predictions. (B)</p> Signup and view all the answers

<p>Feature extraction transforms features into a new space, while feature selection removes irrelevant ones. (B)</p> Signup and view all the answers

<p>To adjust hyperparameters and prevent overfitting. (B)</p> Signup and view all the answers

<p>To increase the size and diversity of the dataset. (A)</p> Signup and view all the answers

<p>SVM works better with small datasets. (A), SVM handles non-linear data with less computational cost. (B), SVM is less prone to overfitting than Neural Networks. (D)</p> Signup and view all the answers

<p>The proportion of correctly identified positives out of all predicted positives. (B)</p> Signup and view all the answers

<p>To optimize the model's architecture and performance (A), To improve generalization on unseen data (C)</p> Signup and view all the answers

<p>The majority class can dominate the voting process. (B)</p> Signup and view all the answers

<p>It converts raw model outputs into probabilities that sum to 1. (B)</p> Signup and view all the answers

<p>To evaluate the model's ability to generalize to unseen data. (B)</p> Signup and view all the answers

<p>Accuracy (C), Macro F1-Score (D)</p> Signup and view all the answers

<p>Background noise can interfere with feature extraction. (A), Real-world audio lacks clear emotion labels. (B), Real-world audio has inconsistent signal lengths. (D)</p> Signup and view all the answers

<p>It determines the type of decision boundary used to separate classes. (A), It defines how data is transformed into a higher-dimensional space. (D)</p> Signup and view all the answers

<p>They can process sequences and time-dependent patterns. (A)</p> Signup and view all the answers

<p>It can cause the model to ignore minority classes. (B), It skews metrics like accuracy toward majority classes. (D)</p> Signup and view all the answers

<p>To speed up the training process and stabilize learning (B)</p> Signup and view all the answers

<p>Training models using labeled data to predict specific outcomes (B)</p> Signup and view all the answers

<p>Identifying patterns in unlabeled data (C)</p> Signup and view all the answers

<p>Sorting data into one of two possible categories (C)</p> Signup and view all the answers

<p>A process of grouping similar data points (B)</p> Signup and view all the answers

<p>Classifying data into more than two categories (B)</p> Signup and view all the answers

<p>A supervised learning algorithm that predicts a data point's label based on its nearest neighbors (B)</p> Signup and view all the answers

<p>A model designed for analyzing sequential data like time-series or speech (B)</p> Signup and view all the answers

<p>An algorithm that relies on fixed rules for decision-making (A)</p> Signup and view all the answers

<p>A neural network optimized for visual data and signal processing (B)</p> Signup and view all the answers

One complete pass through the entire dataset during training.

Validation error shows how the model performs on unseen data. If validation error increases while training error decreases, it indicates overfitting.

Training data is used to train the model. Validation data is used to tune the model during training. Test data is used only after training to evaluate the model's generalization.

Regularization, dropout, data augmentation, and early stopping.

MFCC summarizes the most important characteristics of audio, especially those relevant to human speech. It reduces data complexity, making it easier for the model to learn.

A table showing correct and incorrect predictions for each class. It can help identify specific errors in emotion recognition (e.g., sad is misclassified as angry).

Precision: How many predicted positives were correct?

Inference is when the model makes predictions based on new input data. In your project, it is used to classify emotions such as happy, angry, etc., from new audio recordings.

Cross-entropy is a measure that calculates the difference between the predicted probabilities and the actual probabilities in a classification problem. It's used as a loss function because it penalizes incorrect predictions more heavily than other methods.

Gradient descent is an optimization algorithm used to train neural networks by adjusting the model's weights to minimize the loss function. It works by taking small steps in the direction of the steepest descent of the loss function.

The learning rate in gradient descent controls the size of the steps taken to adjust model weights. A high learning rate can make the model overshoot the optimal solution, while a low learning rate slows down the training process.

Regression models output continuous values, such as temperature or height, while classification models predict the probability of an input belonging to a specific category.

Neural networks can have multiple inputs and outputs, allowing them to process complex data and make multiple predictions simultaneously.

Training a model should stop when the training error plateaus or starts to increase, indicating that the model is no longer improving and may be overfitting the training data.

A general model inspired by the brain's structure, used for learning patterns in data.

A specific type of neural network with many layers, capable of learning complex patterns from large datasets.

A simple neural network with only a few layers, suitable for basic tasks.

A complex neural network with many layers, designed for handling intricate tasks and utilizing large datasets.

A table that displays the performance of a classification model. It compares the model's predictions against the actual values.

The model correctly identified a positive class (e.g., correctly predicting a spam email).

The model correctly identified a negative class (e.g., correctly predicting a non-spam email).

The model predicted a positive class when it was actually negative (e.g., incorrectly classifying a non-spam email as spam).

Regression is a machine learning technique that uses past data to predict future outcomes. It works by finding a formula that fits the patterns in the data and then using that formula to make predictions on new data.

To make training more manageable, large datasets are split into smaller parts called batches. An epoch completes when the model has processed all the batches and seen the entire dataset.

During training, after each epoch, the model updates its parameters (weights and biases) to improve its performance based on what it has learned. By adjusting these parameters, the model becomes better at making accurate predictions.

Training typically requires multiple epochs so the model can refine its understanding of the data and improve its predictions. The more epochs, the better the model generally performs.

Overfitting occurs when a model memorizes the training data too well, resulting in poor performance on new data. This happens if the model trains for too many epochs and focuses too much on the specific details of the training set.

A neural network is a type of machine learning model that is inspired by the structure of the human brain. It consists of interconnected layers of "neurons" that process data and learn patterns.

Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data. It excels at tasks like image recognition, natural language processing, and speech recognition.

The percentage of correct predictions made by the model. It measures the overall accuracy of the model.

Of all the positive predictions made by the model, how many were actually correct. It shows how precise the model is at identifying true positives.

Of all the actual positive cases, how many did the model correctly predict as positive. It measures the model's ability to find all the true positive cases.

A balanced measure of Precision and Recall, calculated as the harmonic mean of the two. A higher F1-score indicates better performance.

The data used to train the model. The model learns patterns and relationships from this data.

The data used to evaluate the model's performance after training is complete. It helps determine how well the model generalizes to new, unseen data.

A technique used to extract meaningful features from audio signals, primarily used in speech and audio processing. It transforms raw audio into a representation highlighting characteristics of human speech or sounds.

The process where the audio signal is divided into small time segments to analyze the sound over time. Each segment is then analyzed to extract its frequency components.

A machine learning approach is preferred for predicting conveyor belt life using sensor data like sound, vibration, and electrical current.

False. The process of adjusting a model's internal settings using known data is called 'training' or 'retraining'. Prediction serving refers to using a trained model to make predictions.

True. Edge AI involves running a machine learning model directly on a device (e.g., a smartphone) rather than sending data to a remote server.

Reduced network latency is the main advantage because it allows for immediate responses. The safety device needs to act quickly to prevent accidents.

It falls under the 'Object Detection' category. The system needs to pinpoint the location of limbs in an image and count them.

Machine learning algorithms can identify intricate patterns within data and predict the remaining life of the conveyor belt.

Training involves updating a model's internal parameters by exposing it to collected, known-good data.

Prediction serving is the process of using a trained model to generate predictions on new data. It doesn't involve adjusting the model parameters.