Notes, summaries, assignments, exams, and problems

Social Justice: Definition and Explanation

Definition: Social justice is the moral and social principle that seeks to ensure fair access for all people and groups to the goods, opportunities, and rights necessary for human dignity.

Explanation: It goes beyond material equality; it aims at equity, solidarity, and respect for human dignity.

It ensures that everyone can participate in the economic, political, and social life of the community.

In Catholic Social Teaching, it is rooted in love of neighbor and the dignity of the human person, not in ideology.

The Principle of Subsidiarity

Definition: A principle that states a higher authority should not interfere in what a lower level can accomplish by itself, but should support (subsidium) when necessary.... Continue reading "Core Principles of Catholic Social Teaching" »

Syllabus Highlights and Course Introduction

Definition and Nature of Learning

Learning is defined as an enduring change in behavior mechanisms resulting from experience. It is crucial to understand the difference between learning and performance: performance changes do not necessarily indicate learning, and learning can occur without visible performance improvements.

Associative Learning involves forming associations between stimuli/events (CS-US) or behavior and outcomes (R-S). Learning can be conscious or unconscious (procedural/implicit learning) and supports emotions, motivations, decision-making, and survival adaptation.

Types of Learning

• Associative Learning: Predictive learning is observed in performance, but changes in performance do not

What is Data Science?

• An interdisciplinary field combining statistics, computer science, and business knowledge.
• Its goal is to extract valuable insights and knowledge from data (both structured and unstructured).
• It answers key business questions: what happened, why, what will happen, and what to do about it.
• The process involves collecting, cleaning, processing, analyzing, and communicating data insights.

Statistical Inference: Making Educated Guesses

• It's the process of using sample data to make educated guesses or draw conclusions about a much larger population.
• Essentially, it lets you make generalizations about a whole group based on a smaller part of it.

Key Goals of Statistical Inference

• Estimation: To guess the value of a population parameter

The Gilded Age: Duality of Progress and Conflict

The Gilded Age, a derogatory term coined by Mark Twain and Charles Dudley Warner, describes the era stretching from the end of the Civil War to the turn of the century. This period was marked by extraordinary economic expansion and industrial innovation that masked deep-seated social problems, including political corruption and massive inequality. Simultaneously, the final push of Manifest Destiny irrevocably altered the geography and demographics of the continent.

The late 19th century was therefore characterized by a duality: the material progress driven by rapid industrialization alongside intensifying struggles over wealth, labor, and the true meaning of American freedom and continental reach.... Continue reading "America's Gilded Age: Progress, Inequality, and Western Expansion" »

The Reconstruction Era: Redefining Freedom and Citizenship

The period following the Civil War, known as Reconstruction, was a revolutionary and highly contested era dedicated to rebuilding the shattered American nation and fundamentally redefining American freedom and citizenship. Initiated by the end of slavery, which freed four million formerly enslaved African Americans, Reconstruction became a crucial struggle between contrasting visions for the postwar South.

The ensuing conflicts involved momentous constitutional changes intended to recognize Black Americans as citizens and guarantee equality before the law, ushering in a brief period of interracial democracy. However, these transformative efforts faced persistent violent resistance from... Continue reading "America's Unfinished Revolution: The Reconstruction Era" »

Talent Acquisition and Candidate Selection

Key Definitions

• Recruitment: Attracting potential candidates.
• Selection: Evaluating and choosing the right candidate.

The ASA Model (Attraction-Selection-Attrition)

• Attraction: People are drawn to organizations with similar values.
• Selection: Organizations hire those who fit the culture and job requirements.
• Attrition: Misfits leave, leading to cultural homogeneity (risk: reduced diversity).

Concept of "Fit"

• Person–Job Fit: Matching skills against job requirements.
• Person–Organization Fit: Alignment of individual values with organizational culture.

Why Fit Matters

Higher performance, satisfaction, and retention. However, too much focus on fit can hurt diversity and innovation.

Recruitment and Selection Strategies

• Recruitment

Understanding the Instance Relationship

In Artificial Intelligence and knowledge representation, the "instance" or "instantiates" relationship describes the connection between an individual object (an instance) and the class or concept (the type) it belongs to.

Explanation of Instance Relationship

• An instance is a specific object or entity that belongs to a broader category or class. For example, "Snoopy" is an instance of the class "Dog."
• The instantiates relation links this individual object to the class it is part of. It shows that the object "is a specific example of" that class.
• This is different from the "is-a" (ISA) or subclass relationship, which connects broader categories or classes to more specific subclasses. The instance relation connects

Define Machine Learning. Briefly explain the types of learnings.

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn automatically from data and improve their performance on a task without being explicitly programmed. It focuses on developing algorithms that can identify patterns and make predictions or decisions.

Types of Learning in Machine Learning:

1. Supervised Learning:

   • The model is trained using labeled data (input-output pairs).

   • It learns the relationship between input and output to make predictions.

   • Examples: Classification (e.G., spam detection), Regression (e.G., price prediction).

2. Unsupervised Learning:

   • The model is trained using unlabeled data (no predefined output).

   • It finds hidden patterns or

What is a Data Structure?

A data structure is a specialized format for organizing, processing, retrieving, and storing data. It enables efficient access and modification of data, making it a fundamental concept in computer science and programming. Data structures are essential for managing large amounts of data, supporting various operations such as searching, sorting, insertion, deletion, and traversal.

Classification of Data Structures

Data structures can be broadly classified into two categories: primitive and non-primitive.

1. Primitive Data Structures

These are the basic data types provided by programming languages. They serve as the building blocks for more complex data structures. Examples include:

• Integer
• Float
• Character
• Boolean

2. Non-Primitive

Question Structures for English Exams

1. Subject Questions

The question word (who/what) acts as the subject of the sentence.

• No auxiliary verb do/does/did is used (except with be or modals).
• The main verb is in its normal affirmative form.

Structure: Question word (who/what) + verb + complement?

2. Object Questions

The question word (who/what/which/whom) acts as the object of the sentence.

• We use an auxiliary verb do/does/did (for simple tenses).
• The subject comes after the auxiliary.

Structure: Question word + auxiliary + subject + verb (base form) + complement?

Essential English Verb Tenses

1. Present Simple

Structures:

• Affirmative: Subject + verb (present simple) + complement
• Negative: Subject + auxiliary do/does + not + verb (base form) + complement (