Self-assessment quiz

# What is Artificial Intelligence (AI)? - [x] A family of techniques aimed at extracting knowledge from data to solve automation tasks > ✅ AI involves methods for analyzing data to create systems that can automate complex tasks. - [ ] A specific type of algorithm designed for image processing > ❌ AI is a broad field encompassing more than image processing algorithms. - [x] A concept that encompasses machine learning (ML) and deep learning (DL) > ✅ ML and DL are subsets of AI, focusing on learning from data and solving specific tasks. - [ ] A single mathematical model used for decision-making > ❌ AI includes many models and techniques, not just one. - [ ] A deterministic system that does not rely on probabilities > ❌ AI often includes probabilistic systems, especially in machine learning and generative models. # What distinguishes Machine Learning (ML) from traditional algorithms? - [x] ML learns from data instead of being explicitly programmed > ✅ Machine learning systems learn patterns from data rather than relying solely on pre-defined rules. - [ ] ML always guarantees the correct solution to a problem > ❌ Machine learning does not always guarantee correctness, as it depends on the data and model used. - [x] ML involves a training phase to estimate parameters > ✅ Machine learning models are trained using datasets to optimize parameters for better predictions. - [ ] ML requires deterministic algorithms > ❌ ML often involves probabilistic models and algorithms. - [ ] ML only works with structured data > ❌ ML can handle both structured and unstructured data, such as images, audio, and text. # What is supervised learning? - [x] A type of ML where the model learns from labeled data > ✅ Supervised learning involves training models on datasets where both inputs and corresponding outputs are known. - [ ] A method to group similar data points without labels > ❌ This describes unsupervised learning, not supervised learning. - [x] A task where the model predicts outputs based on examples > ✅ Supervised learning models are trained to predict outputs using labeled input-output pairs. - [ ] A process where the model receives rewards for actions > ❌ This describes reinforcement learning, not supervised learning. - [ ] A method exclusive to decision trees > ❌ Supervised learning is not restricted to decision trees; it includes various models like linear regression and neural networks. # What is a feature in machine learning? - [x] An individual measurable property of the data > ✅ Features are the attributes or variables used as input to machine learning models. - [ ] A type of deep learning algorithm > ❌ Features are not algorithms; they are data attributes. - [x] A representation of data used for training a model > ✅ Features are the representation of input data that models use for learning. - [ ] A matrix of output predictions > ❌ Features are input properties, not outputs. - [ ] A hyperparameter in neural networks > ❌ Hyperparameters control the model's training process, while features represent input data. # Why is dimensionality reduction important in machine learning? - [x] It simplifies the data by reducing the number of features > ✅ Dimensionality reduction removes irrelevant or redundant features, making the data easier to process. - [ ] It guarantees better model performance > ❌ Dimensionality reduction improves efficiency but does not always guarantee better performance. - [x] It helps in visualizing high-dimensional data > ✅ Reducing dimensions enables better visualization of data in 2D or 3D space. - [ ] It eliminates the need for feature selection > ❌ Dimensionality reduction complements feature selection but does not replace it entirely. - [ ] It automatically trains the model > ❌ Dimensionality reduction is a preprocessing step, not a model training process.

# What are examples of object detection? - [x] Detecting and localizing objects in an image with bounding boxes > ✅ Object detection identifies and localizes instances of objects within an image, often using bounding boxes and probabilities. - [ ] Translating text in an image > ❌ Text translation involves natural language processing, not object detection. - [x] Using YOLO or DETR models to identify objects in an image > ✅ YOLO and DETR are popular models for object detection tasks. - [ ] Segmenting a tumor in medical imaging > ❌ This is an example of semantic segmentation, not object detection. - [ ] Predicting the age of an individual from an image > ❌ This is a classification task, not object detection. # What are common applications of large language models (LLMs)? - [x] Translation, summarization, and question answering > ✅ LLMs excel in natural language understanding tasks such as translation, text summarization, and answering questions. - [ ] Analyzing audio for sound events > ❌ Audio analysis falls under signal processing, not LLM tasks. - [x] Conversational AI and role-playing > ✅ LLMs are widely used in chatbots and systems designed for interactive conversations. - [ ] Classifying images based on their content > ❌ Image classification is a task in computer vision, not NLP. - [x] Software coding assistance (e.g., GitHub Copilot) > ✅ LLMs like GitHub Copilot are used to assist in software coding by generating relevant code snippets. # Which application involves time series forecasting? - [x] Predicting future energy demands > ✅ Time series forecasting predicts future values, such as energy demands, based on historical data. - [ ] Segmenting brain signals in EEG recordings > ❌ Segmentation involves dividing data into distinct parts, not forecasting future trends. - [x] Weather prediction > ✅ AI is commonly used to forecast weather based on time series data. - [ ] Recognizing bird species from their sounds > ❌ Recognizing bird species involves audio classification, not time series forecasting. - [ ] Generating music from an input melody > ❌ Music generation does not rely on time series forecasting techniques. # Which tasks are addressed by audio-based AI? - [x] Sound event detection > ✅ Sound event detection identifies the start and end times of specific sounds in audio recordings. - [x] Marine mammal identification > ✅ AI can identify marine mammal species from underwater acoustic recordings. - [ ] Translating spoken words between languages > ❌ Translation falls under NLP tasks, not audio analysis. - [x] Musical source separation > ✅ AI can separate the instrumental and vocal tracks in a song. - [ ] Classifying tumors in medical imaging > ❌ This task is part of computer vision, not audio processing.

# What are the main differences between supervised and unsupervised learning? - [x] Supervised learning uses labeled data, while unsupervised learning uses unlabeled data > ✅ Supervised learning requires labeled datasets for training, whereas unsupervised learning identifies patterns without labels. - [ ] Supervised learning is faster than unsupervised learning > ❌ The speed depends on the algorithm and the data, not necessarily on whether it's supervised or unsupervised. - [x] Unsupervised learning groups data into clusters > ✅ Unsupervised learning often involves clustering data into meaningful groups. - [ ] Supervised learning does not require a loss function > ❌ Supervised learning optimizes a loss function to minimize prediction errors. - [ ] Unsupervised learning uses a training set for prediction > ❌ Unsupervised learning identifies patterns and structures without making predictions. # What is the purpose of the k-nearest neighbors (k-NN) algorithm? - [x] To predict labels based on the closest training samples > ✅ k-NN predicts the label of a query point by examining its k nearest neighbors in the training data. - [ ] To partition data into clusters > ❌ Partitioning data into clusters is a task for algorithms like k-means, not k-NN. - [x] To classify new data points using majority voting among neighbors > ✅ k-NN uses the majority class among the nearest neighbors to classify a query point. - [ ] To train a model by optimizing parameters > ❌ k-NN does not involve training; it relies on storing and comparing with the training dataset. - [ ] To estimate centroids in feature space > ❌ Estimating centroids is a feature of k-means, not k-NN. # Which distance metrics are commonly used in k-NN and k-means? - [x] Euclidean distance > ✅ Euclidean distance is the most commonly used metric for both k-NN and k-means. - [x] Manhattan distance > ✅ Manhattan distance is another valid metric, particularly for grid-like data structures. - [ ] Cosine similarity > ❌ Cosine similarity measures angles, not distances, and is less common in k-NN and k-means. - [ ] Jaccard similarity > ❌ Jaccard similarity is used for set-based comparisons, not for k-NN or k-means. - [ ] Hamming distance > ❌ Hamming distance is used for categorical data, not typical for k-NN or k-means clustering. # What is the complexity of predicting a query in k-NN? - [x] O(Nd), where N is the number of training samples and d is the feature dimension > ✅ Prediction involves calculating distances from the query to all training points, making it O(Nd). - [ ] O(log N) > ❌ Logarithmic complexity is not applicable to k-NN, which requires scanning all points. - [x] Computationally expensive for large datasets > ✅ Since prediction requires comparing with all training points, k-NN is computationally expensive for large datasets. - [ ] Constant time for all datasets > ❌ Prediction time grows with the size of the training dataset and the dimensionality of features. - [ ] Independent of the feature dimension > ❌ Feature dimension directly impacts the computational cost of distance calculations.

# What is the primary focus of deep learning? - [x] Learning directly from raw data with minimal preprocessing > ✅ Deep learning methods learn features directly from raw data, reducing the need for manual feature extraction. - [ ] Using only pre-extracted features for training models > ❌ Deep learning performs end-to-end learning, including feature extraction. - [x] Training models with automatic differentiation and backpropagation > ✅ Deep learning relies on automatic differentiation and backpropagation to optimize model parameters. - [ ] Working exclusively with labeled data > ❌ Deep learning can work with both labeled and unlabeled data, especially in self-supervised learning. - [ ] A manual process of optimizing each layer > ❌ Deep learning optimizes all layers simultaneously during training. # What is a fully connected layer in a neural network? - [x] A layer where each node is connected to all nodes in the previous layer > ✅ Fully connected layers compute a weighted sum of all inputs, followed by an activation function. - [ ] A layer that operates only on image data > ❌ Fully connected layers are general and not limited to image data. - [ ] A layer with shared weights > ❌ Shared weights are characteristic of convolutional layers, not fully connected layers. - [x] Used in multi-layer perceptrons (MLPs) > ✅ Fully connected layers form the basis of MLPs, often used for structured data tasks. - [ ] The final output layer of every neural network > ❌ Not all networks end with fully connected layers; some tasks use specialized layers like softmax. # What are the main functions of convolutional layers? - [x] To capture spatial patterns in data like images > ✅ Convolutional layers extract spatial features using small filters, making them suitable for image and audio data. - [ ] To connect all neurons in the previous layer > ❌ This describes fully connected layers, not convolutional layers. - [x] To share weights across different regions of the input > ✅ Convolutional layers use shared weights (kernels) to efficiently process data with regular structure. - [ ] To process unstructured text data > ❌ Convolutional layers are typically used for structured data like images, not unstructured text. - [ ] To reduce the size of the input > ❌ Reducing input size is typically the role of pooling layers, not convolutional layers. # What is automatic differentiation in deep learning? - [x] Computing gradients automatically for all layers > ✅ Automatic differentiation calculates gradients efficiently, enabling the training of deep networks using backpropagation. - [ ] Optimizing hyperparameters > ❌ Hyperparameter optimization is separate from automatic differentiation, which focuses on computing gradients. - [x] A key step in backpropagation > ✅ Automatic differentiation is essential for backpropagation, which updates weights based on gradient descent. - [ ] A method for creating new neural architectures > ❌ Automatic differentiation is not for designing architectures; it facilitates efficient training. - [ ] Replacing all manual computations in neural networks > ❌ While it automates gradient computations, other manual decisions (e.g., architecture design) remain essential.