Computer Science Artificial Intelligence

Artificial Intelligence (AI) is the branch of computer science that aims to create systems capable of performing tasks that require human intelligence. These tasks include problem-solving, learning, reasoning, perception, and language understanding.

• Supervised Learning: Involves training a model on labeled data, meaning the input comes with the correct output. The model learns to map inputs to outputs.
• Unsupervised Learning: Involves training a model on data without labeled responses and is used to find patterns and structures within the data.
• Reinforcement Learning: Involves training a model through rewards and punishments. The model learns to make a sequence of decisions by receiving feedback from its actions in the environment.

A neural network is a series of algorithms that attempt to recognize relationships in a set of data through a process that mimics the way the human brain operates. It consists of layers of nodes (neurons), with each layer performing different levels of feature extraction.

Backpropagation is a supervised learning algorithm used for training neural networks. It involves two phases:

• Forward Pass: Calculating the predicted output based on the current weights.
• Backward Pass: Calculating the gradient of the loss function with respect to each weight by the chain rule, then updating the weights to minimize the loss.

Overfitting occurs when a model learns the training data too well, including the noise and outliers, which negatively impacts its performance on new data. Preventative measures include:

• Using more training data
• Implementing regularization techniques (e.g., L1, L2)
• Using dropout in neural networks
• Pruning decision trees
• Cross-validation techniques

A confusion matrix is a table used to evaluate the performance of a classification algorithm. It summarizes the results of predictions by comparing the actual and predicted classifications:

• True Positives (TP): Correctly predicted positive cases
• True Negatives (TN): Correctly predicted negative cases
• False Positives (FP): Incorrectly predicted positive cases (Type I error)
• False Negatives (FN): Incorrectly predicted negative cases (Type II error)

• Convolutional Neural Networks (CNNs): Primarily used for image recognition and processing.
• Recurrent Neural Networks (RNNs): Suitable for sequential data, like time series or natural language.
• Generative Adversarial Networks (GANs): Used for generating new data samples similar to a given dataset.
• Long Short-Term Memory Networks (LSTMs): A type of RNN capable of learning long-term dependencies.

CNNs are designed to process and recognize images. They consist of several layers:

• Convolutional Layers: Apply filters to the input image to detect features.
• Pooling Layers: Reduce the dimensionality of the data by combining the outputs of neuron clusters.
• Fully Connected Layers: Flatten the data and connect every neuron from the previous layer to the next layer, often used for final classification.

[Provide a detailed description of a project you’ve worked on, including the problem, the data, the model you used, the challenges you faced (such as data preprocessing, model tuning, etc.), and the solutions you applied.]

Feature selection can be approached using various methods:

• Filter Methods: Selecting features based on statistical measures (e.g., chi-square, correlation).
• Wrapper Methods: Using a model to test different combinations of features and evaluating performance.
• Embedded Methods: Feature selection occurs during the model training process (e.g., Lasso regression).