Introduction to AI: Key Concepts and Applications

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Introduction to AI: Key Concepts and Applications

Name: Introduction to AI: Key Concepts and Applications
Rating: 4.84 (25 reviews)

Instructor: Ian McCulloh

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6 modules

Gain insight into a topic and learn the fundamentals.

25 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

6 modules

Gain insight into a topic and learn the fundamentals.

25 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

What you'll learn

Understand core AI and ML concepts, key vocabulary, and the R.O.A.D. Framework for effective AI project management and implementation.
Evaluate machine learning models using performance metrics and understand the tradeoffs in algorithm selection and optimization.
Analyze AI algorithms like SVM, Decision Trees, and Neural Networks, identifying their strengths, weaknesses, and practical applications.
Assess data quality, calculate inter-annotator agreement, and address resource and performance tradeoffs in AI and ML systems.

Skills you'll gain

Tools you'll learn

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Assessments

15 assignments

Taught in English

Flexible schedule

Learn at your own pace

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There are 6 modules in this course

The course "Core Concepts in AI" provides a comprehensive foundation in artificial intelligence (AI) and machine learning (ML), equipping learners with the essential tools to understand, evaluate, and implement AI systems effectively. From decoding key terminology and frameworks like R.O.A.D. (Requirements, Operationalize Data, Analytic Method, Deployment) to exploring algorithm tradeoffs and data quality, this course offers practical insights that bridge technical concepts with strategic decision-making.

What sets this course apart is its focus on balancing technical depth with accessibility, making it ideal for leaders, managers, and professionals tasked with driving AI initiatives. Learners will delve into performance metrics, inter-annotator agreement, and tradeoffs in resources, gaining a nuanced understanding of AI's strengths and limitations. Whether you're a newcomer or looking to deepen your understanding, this course empowers you to make informed AI decisions, optimize systems, and address challenges in data quality and algorithm selection. By the end, you'll have the confidence to navigate AI projects and align them with organizational goals, positioning yourself as a strategic leader in AI-driven innovation.

This course provides a comprehensive introduction to key concepts in artificial intelligence (AI) and machine learning (ML). Learners will explore essential vocabulary, the R.O.A.D. Framework, performance evaluation, and algorithm tradeoffs. Topics include data quality, inter-annotator agreement, and the strengths and weaknesses of AI methods. By the end, learners will be equipped with the foundational knowledge to navigate and assess AI and ML systems effectively.

What's included

1 reading1 plugin

This module provides an introduction to artificial intelligence (AI). It does not require any prior knowledge of AI and is suitable for briefing managerial, and non-technical leaders to improve knowledge, expectations, and communication for AI projects.

What's included

6 videos3 readings3 assignments

6 videosTotal 120 minutes

Introduction to Artificial Intelligence (AI)40 minutes
Applications of AI23 minutes
AI Project Management Frameworks14 minutes
R.O.A.D. Framework - Requirements8 minutes
R.O.A.D. Framework - Operationalize Data30 minutes
R.O.A.D. Framework - Analytic Methods & Deployment5 minutes

3 readingsTotal 140 minutes

Reading References60 minutes
Self-Reflective Reading: AI Project Management Scenario - The SmartGrocery Shopper Assistant40 minutes
Self-Reflective Reading: Reflective Essay40 minutes

3 assignmentsTotal 90 minutes

Introduction to Artificial Intelligence60 minutes
Exploring AI Fundamentals and Project Management15 minutes
Mastering the R.O.A.D. Framework for AI Projects15 minutes

This module covers the statistical foundations of machine learning and the common metrics for evaluating machine learning and artificial intelligence performance.

What's included

6 videos1 reading3 assignments

6 videosTotal 63 minutes

Steps in Hypothesis Testing15 minutes
Two Sample T-Testing15 minutes
Analysis of Variance (ANOVA) 2 minutes
Types of Error in Statistical Estimation12 minutes
Machine Learning Performance - Part 113 minutes
Machine Learning Performance - Part 26 minutes

1 readingTotal 10 minutes

Readings References10 minutes

3 assignmentsTotal 90 minutes

Machine Learning60 minutes
Foundations of Hypothesis Testing and Statistical Analysis15 minutes
Understanding Errors and Performance Metrics in Machine Learning15 minutes

This module introduces the most common algorithms used in AI and machine learning, including support vector machines, Naïve Bayes, decision trees, random forest, and neural networks. We will discuss the strengths and weaknesses of these algorithms for different classes of problems.

What's included

8 videos1 reading3 assignments

8 videosTotal 64 minutes

Algorithm Tradeoffs5 minutes
Support Vector Machines7 minutes
Naïve Bayes5 minutes
Decision Trees10 minutes
Random Forest4 minutes
Neural Networks15 minutes
Unsupervised Learning3 minutes
Algorithm Tradeoff Scenarios13 minutes

1 readingTotal 30 minutes

Reading References30 minutes

3 assignmentsTotal 90 minutes

Algorithm Tradeoffs 60 minutes
Exploring Supervised Learning and Algorithm Tradeoffs15 minutes
Advanced Algorithms and Tradeoff Scenarios15 minutes

This module explores data types (nominal, ordinal, categorical) and the challenges of data labeling, including human cognitive limits and reference issues. A key focus is inter-annotator agreement—a method to measure labeling consistency, highlighting biases and inefficiencies in human and machine processes. Consistent labeling, often more impactful than advanced algorithms, is crucial for responsible AI.

What's included

9 videos1 reading3 assignments

9 videosTotal 90 minutes

Introduction to Data16 minutes
Data Examples 2 minutes
Labeling11 minutes
Labeling Challenges7 minutes
Labeling Considerations7 minutes
Cognitive Limitations11 minutes
Inter-Annotator Agreement (IAA)11 minutes
Krippendorf’s Alpha11 minutes
Size-Consistency-Quality Tradeoffs15 minutes

1 readingTotal 30 minutes

Reading References30 minutes

3 assignmentsTotal 90 minutes

Data - Assessments60 minutes
Data Basics and Labeling Techniques15 minutes
Data Quality and Annotation Standards15 minutes

This module introduces the most common resource considerations in AI, specifically memory, computational tradeoffs, query expressiveness, and algorithm performance.

What's included

10 videos1 reading3 assignments

10 videosTotal 102 minutes

Resources3 minutes
Memory15 minutes
Memory Applications and Challenges8 minutes
Memory Management Strategies12 minutes
Computational Tradeoffs - Algorithms10 minutes
Computational Tradeoffs - Parallelization14 minutes
Query Expressiveness9 minutes
Query Expressiveness Examples14 minutes
Query Expressiveness - Practical Implications6 minutes
Performance11 minutes

1 readingTotal 90 minutes

Reading References90 minutes

3 assignmentsTotal 90 minutes

Resources60 minutes
Memory Management and Computational Tradeoffs in Algorithms15 minutes
Computational Tradeoffs, Query Expressiveness, and Performance Optimization15 minutes

Instructor

Instructor ratings

(10 ratings)

Ian McCulloh

Johns Hopkins University

17 Courses29,932 learners

Offered by

Johns Hopkins University

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Reviewed on Nov 22, 2025

A very good Introduction To AI. Thank you Dr. Ian McCulloh and thank you Johns Hopkins!!

Reviewed on Aug 18, 2025

challenging but interesting if you want to learn more intermediate/advanced things on AI

Reviewed on Feb 21, 2025

Very well structured and very informative, much appreciated.

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