Artificial Intelligence Course Overview

A field of computer science that focuses on creating intelligent systems, often imitating human capabilities like problem-solving, learning, and decision-making.

A broad area within AI that focuses on teaching machines to learn from data, without explicit programming. It's a core concept of AI enabling machines to improve their performance over time.

Machine learning algorithms that infer patterns from data without labeled examples. Think clustering data into groups based on similarities. Examples include K-Means and Hierarchical Clustering.

Machine learning algorithms that learn from labeled data, such as the relationship between inputs and outputs. Examples include Decision Trees, K-Nearest Neighbors, and Support Vector Machines.

A type of unsupervised learning that clusters data into K groups, where each group represents similar data points. Think grouping customers based on buying habits.

A type of supervised learning that uses a tree-like structure to represent decisions and outcomes. Think predicting a loan approval based on factors like income and credit score.

A type of supervised learning that classifies new data points based on their similarity to previously labeled data points. Think predicting a movie's genre based on its similarity to previous movies.

A type of algorithm that learns using artificial neural networks, mimicking the structure of the human brain. Often used for complex tasks like image recognition, language translation, and natural language processing.

Reinforcement learning is a type of machine learning where the AI agent learns from its interactions with an environment. It receives rewards for good actions and penalties for bad actions, improving its performance over time.

In reinforcement learning, the AI agent is in a specific state, makes a decision, and receives a reward or penalty from the environment based on its action. This feedback loop helps the agent learn.

This learning method is iterative, where the AI agent repeatedly interacts with the environment, updating its understanding of the world based on the received rewards and penalties.

Supervised Learning requires labeled data, meaning each data point has a known output or target value. The algorithm learns relationships between inputs and outputs to predict new data points.

This method uses both labeled and unlabeled data. The algorithm uses the labeled data to learn initial patterns and then uses this knowledge to predict the labels for the unlabeled data.

A field of computer science focused on creating algorithms that enable machines to 'learn' from data, improving their performance in solving specific tasks without being explicitly programmed for each scenario.

The set of data used to train a machine learning model, containing a collection of examples and their corresponding outcomes.

The set of data used to evaluate the performance of a trained machine learning model, providing an unbiased assessment of how well the model generalizes to unseen data.

A collection of data points, often structured in rows (observations) and columns (attributes or features), used as input for machine learning models.

The process of transforming raw data into a suitable format for machine learning algorithms, often involving cleaning, normalization, and feature engineering.

Replacing missing values in a dataset with reasonable estimates or removing instances with missing values.

Transforming features in a dataset to have a specific range or distribution, often useful for improving model performance.

Identifying and removing instances in a dataset that differ significantly from other data points, often caused by errors or anomalies.

Converting continuous features into discrete categories, often useful for simplifying data and improving model interpretability.

The process of extracting useful features from raw data, often involving transforming or combining existing features to improve model performance.

Selecting a subset of features from a dataset to improve model efficiency and performance.

The process of training a machine learning model by feeding it with data and allowing it to learn patterns and relationships from the data.

The process of evaluating the performance of a trained machine learning model on unseen data, providing insights into its generalization ability.

The process of making a trained machine learning model available for use in a real-world application, often involving deploying the model to a server or integrating it into existing systems.

A process of building a model from a data space where rows represent samples and columns represent descriptors.

The process of evaluating a model's performance and accuracy before using it in real-world scenarios. It includes steps like calculating error rates, improving the model based on results, and seeking expert validation.

A representation of data relationships in a database, used for visualization, description, classification, structuring, explanation, or prediction.

The process of preparing data for machine learning by cleaning, transforming, and selecting relevant features.

A step in data preparation that involves converting continuous attributes into categorical ones, such as changing age to age groups.

A step in data preparation that involves removing outliers or unusual data points from the dataset.

A step in data preparation that involves scaling data values to a common range, ensuring that all attributes have a similar scale.

A step in data preparation that involves selecting the most informative features that contribute significantly to the learning task.

A type of supervised learning where the model predicts a categorical output, such as classifying emails as spam or not spam.

A type of supervised learning where the model predicts a continuous output, such as predicting house prices or temperature.

A type of unsupervised learning that groups similar data points together into clusters based on their features.

A type of unsupervised learning that reduces the dimensionality of data by finding principal components, which capture the most variance in the data.