Introduction to Applied Artificial Intelligence - ACCT 331 Lecture Summary

These flashcards summarize key concepts, processes, and methodologies discussed in the Introduction to Applied Artificial Intelligence course (ACCT 331), to aid in your study and preparation for upcoming assessments.

What is the primary focus of Applied Artificial Intelligence in business contexts?

To enhance business decision-making, optimize operations, and develop AI-driven applications.

What are the foundational skills introduced in Week 2 of the course?

Basic principles of AI, machine learning algorithms, and data handling techniques.

What are the core areas covered in Weeks 3 and 4?

Machine Learning applications specific to business contexts, focusing on practical implementation.

What key processes are outlined for data preparation in machine learning?

Data cleaning, encoding, feature engineering, and class label imbalance handling.

Define Exploratory Data Analysis (EDA) in the context of machine learning.

EDA refers to techniques for analyzing datasets to summarize their main characteristics, often using visual methods.

What are some of the methods used in Exploratory Data Analysis?

Methods include descriptive statistics, data visualization (histograms, scatter plots, box plots), and handling missing data.

What is the purpose of a data dictionary?

To document data attributes, ensuring clarity, consistency, and effective management of data.

Explain the concept of feature engineering.

Feature engineering involves transforming raw data into meaningful features that improve the predictive performance of models.

What is the curse of dimensionality?

The challenges that arise when analyzing high-dimensional data, leading to issues like data sparsity and increased complexity.

What are two common techniques for feature selection in machine learning?

Filter methods, which rank features based on statistical measures, and embedded methods, which are integrated into the model training process.

What does PCA stand for and what is its purpose?

Principal Component Analysis; it is used for dimensionality reduction while preserving variance in the dataset.

During which week is Exam #1 scheduled?

Exam #1 is scheduled for Week 6, on 9/30/25.

What are the key blockers in the adoption of AI technologies mentioned in the notes?

Lack of transparency, algorithmic bias, insufficient AI infrastructure, and ethical risks.

How are preferences for partnering with AI providers reflected in the data?

Most organizations prefer consumption-based pricing and partnering with established providers like Salesforce over building in-house.