Introduction to AI: Key Concepts and Applications

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Introduction to AI: Key Concepts and Applications

位教师：Ian McCulloh

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6个模块

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6个模块

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中级等级

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2 周完成

在 10 小时一周

灵活的计划

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您将学到什么

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.

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作业

15 项作业

授课语言：英语（English）

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该课程共有6个模块

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.

涵盖的内容

1篇阅读材料1个插件

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.

涵盖的内容

6个视频3篇阅读材料3个作业

6个视频总计120分钟

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

3篇阅读材料总计140分钟

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

3个作业总计90分钟

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

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

涵盖的内容

6个视频1篇阅读材料3个作业

6个视频总计63分钟

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

1篇阅读材料总计10分钟

Readings References10分钟

3个作业总计90分钟

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

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.

涵盖的内容

8个视频1篇阅读材料3个作业

8个视频总计64分钟

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

1篇阅读材料总计30分钟

Reading References30分钟

3个作业总计90分钟

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

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.

涵盖的内容

9个视频1篇阅读材料3个作业

9个视频总计90分钟

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

1篇阅读材料总计30分钟

Reading References30分钟

3个作业总计90分钟

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

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

涵盖的内容

10个视频1篇阅读材料3个作业

10个视频总计102分钟

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

1篇阅读材料总计90分钟

Reading References90分钟

3个作业总计90分钟

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

位教师

授课教师评分

(10个评价)

Ian McCulloh

Johns Hopkins University

17 门课程28,337 名学生

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Johns Hopkins University

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已于 Feb 20, 2025审阅

Very well structured and very informative, much appreciated.

已于 Nov 21, 2025审阅

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

已于 Aug 17, 2025审阅

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

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