Introduction to Deep Learning & Neural Network

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Deep learning is a subset of machine learning that focuses on neural networks with multiple layers (deep neural networks). It has led to significant advancements in various domains, including computer vision, natural language processing, and reinforcement learning. Neural Network Architectures Deep learning encompasses various neural network architectures, including: Training Deep Neural Networks Training deep neural […]

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Reinforcement Learning

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Agent-Environment Interaction Reinforcement learning involves an agent interacting with an environment and learning to take actions that maximize cumulative rewards over time. Reward Signal The agent receives a reward signal from the environment as feedback for its actions. The goal of the agent is to learn a policy that maximizes the expected cumulative reward. Exploration

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Unsupervised Learning

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Clustering Unsupervised learning algorithms can be used for clustering, where the goal is to partition the data into groups or clusters based on similarity or proximity. Dimensionality Reduction Unsupervised learning techniques such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) are used for dimensionality reduction, where the goal is to reduce the

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Supervised Learning

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Training Data Supervised learning requires labelled training data, where each example in the dataset is paired with its corresponding output label. Classification vs. Regression Supervised learning tasks can be categorized into two main types: Algorithms Supervised learning algorithms include: Evaluation Metrics Common evaluation metrics for supervised learning include accuracy, precision, recall, F1-score, and area under

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Introduction to Machine Learning

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Definition of Machine Learning Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, relying on patterns and inference instead. Types of Machine Learning Machine learning can be categorized into three main types: Applications of Machine Learning Machine

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Expert Systems: Harnessing Human Expertise in AI

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Expert systems are a class of artificial intelligence (AI) systems designed to emulate the decision-making ability of human experts in a specific domain. They combine knowledge representation, inference mechanisms, and user interfaces to solve problems, make decisions, or provide recommendations in a manner similar to that of a human expert. Introduction to Expert Systems Expert

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Frames and Scripts

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Introduction to Frames and Scripts Frames and scripts are knowledge representation schemes that organize knowledge into structured units. Frames represent objects or concepts along with their properties and relationships, while scripts represent stereotypical sequences of events or actions associated with particular situations. Frame Representation Frames consist of slots (attributes or properties) and fillers (values or

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Semantic Networks

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Introduction to Semantic Networks Semantic networks represent knowledge in the form of nodes (representing concepts or objects) and links (representing relationships between concepts). They provide a graphical representation of knowledge that is easy to understand and manipulate. Nodes, Arcs, and Hierarchical Structure Semantic networks consist of nodes connected by arcs, where nodes represent concepts or

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First-Order Logic

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Introduction to First-Order Logic First-order logic, also known as predicate logic, extends propositional logic by introducing variables, quantifiers, and predicates. It allows for a more expressive and precise representation of knowledge and relationships between objects. Variables, Quantifiers, and Predicates First-order logic uses variables to represent objects, quantifiers such as ∀ (for all) and ∃ (there

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Propositional Logic

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Introduction to Propositional Logic Propositional logic, also known as sentential logic, deals with propositions or statements that can be either true or false. It provides a formal framework for reasoning about logical relationships between propositions. Propositional Symbols and Connectives Propositional logic uses symbols to represent propositions and logical connectives to combine propositions into more complex

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