Probabilistic Reasoning & AI Decision Making
Probabilistic Inference
What Is Probability?
A measure
Passigning each eventAa number in[0,1].Axioms of Probability
P(∅) = 0,P(Ω) = 1For disjoint
A, B,P(A∪B) = P(A) + P(B)
Conditional Probability:
P(A|B) = P(A∧B) / P(B)
Bayesian Inference Fundamentals
Bayes’ Rule
P(H|E) = [P(E|H) * P(H)] / P(E)Key Terms in Bayes’ Rule
Prior
P(H): initial beliefLikelihood
P(E|H)Posterior
P(H|E): updated belief
Bayesian Networks (BNs)
Structure: Directed acyclic graph; nodes = variables; edges = dependencies.
Essential Machine Learning Concepts Explained
Regularization Techniques in Machine Learning
Regularization is a set of techniques used to reduce overfitting by encouraging simpler models. It works by adding a penalty term to the loss function that discourages overly large or complex weights. The two most common forms are L2 (Ridge) and L1 (Lasso) regularization.
L2 Regularization (Ridge / Weight Decay)
L2 Regularization adds the squared L2 norm of the weights to the loss: L(w) = original loss + λ‖w‖². This shrinks weights proportionally
Read MoreMachine Learning Fundamentals: Concepts, Types, Applications
What is Machine Learning? How ML Works
Machine Learning (ML) is a subset of Artificial Intelligence (AI) where systems learn from data to make predictions or decisions without being explicitly programmed. It works by following these steps:
- Data Input: Feeding data (e.g., numbers, images) into an algorithm.
- Training: The algorithm identifies patterns and builds a model.
- Validation/Testing: The model is tested on new data to ensure accuracy.
- Deployment: The model makes predictions on real-world data, improving
Machine Learning Fundamentals: Data Analysis & Clustering Techniques
Core Machine Learning Concepts
Linear Regression
Linear Regression: Finds the best line that summarizes the relationship between two variables. Imagine a scatter plot of data points and a line representing their trend.
Dimensionality Reduction
Dimensionality Reduction: Addresses datasets with a large number of variables, which can lead to a complex dispersion matrix. It reduces the number of variables to a more manageable few.
Why Dimensionality Reduction?
- Simpler Analysis: Fewer features make it easier
Essential Cybersecurity Concepts: Risk, Frameworks, and Security
Risk Management Basics
Risk: Probability of loss or disruption. Asset: Item of value (e.g., data, systems). Threat: Entity exploiting a weakness. Vulnerability: Weakness that can be exploited. Risk Management: Identifying, evaluating, reducing, or accepting risks.
Types of Threat Actors
Script Kiddie: Uses pre-written code, lacks technical skill, wants fame. Hacktivist: Politically motivated, defaces sites or networks. Nation State/APT: Government-backed, highly sophisticated. Organized Crime: Seeks
Read MoreJava Object-Oriented Student Management System
This document presents a foundational Java application demonstrating object-oriented programming (OOP) principles through a simple university student management system. It showcases inheritance, polymorphism, and class design to manage different types of students: undergraduates, graduates, and alumni.
Core Class Definitions
The system is built upon a hierarchy of classes, starting with an abstract Student class, which defines common attributes and behaviors for all student types. Specific student
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