AI Academy

Introduction to Social Computing

Instructor: Ian McCulloh Duration: 2 weeks to complete at 10 hours a week
Objective 1 Understand the basics of social computing and its relationship with social media and analytics.
Objective 2 Analyze how social media influences communication, behavior, and societal interactions.
Objective 3 Recognize how cognitive biases affect online behavior and the spread of information.
Objective 4 Explore how gamification enhances user motivation and improves social computing applications.
Usability
Data Ethics
Game Design
Sociology
Analytics
Behavioral Economics
Artificial Intelligence
Data Collection
Machine Learning
Systems Thinking
Social Sciences
Network Analysis
Psychology
Research

Social Network Analysis

Instructor: Ian McCulloh Duration: 13 hours to complete 3 weeks at 4 hours a week
Objective 1 Learn to calculate and interpret key centrality measures to identify influential nodes in social networks.
Objective 2 Gain skills in applying statistical models to analyze relationships and dynamics within social networks.
Objective 3 Understand how foundational social theories inform network analysis and shape interpretations of social interactions.
Network Analysis
Statistical Hypothesis Testing
Statistical Modeling
Trend Analysis
R Programming
Sociology
Social Sciences
Graph Theory
Statistical Analysis

Training AI with Humans

Instructor: Ian McCulloh Duration: 22 hours to complete 3 weeks at 7 hours a week
Objective 1 Learn to construct and evaluate various machine learning classifiers and performance metrics.
Objective 2 Master the calculation and implications of Inter-Annotator Agreement (IAA) for data consistency.
Objective 3 Understand how to design and implement effective crowdsourcing tasks using Amazon Mechanical Turk.
Objective 4 Analyze crowdsourced data to enhance machine learning models and understand ethical considerations in AI.
Data Validation
Performance Testing
Machine Learning
Data Ethics
Experimentation
Statistical Analysis
Human Machine Interfaces
Applied Machine Learning
Data Quality
Research Design
Data Collection
Artificial Intelligence and Machine Learning (AI/ML)

Chatbots

Instructor: Ian McCulloh Duration: 1 week to complete at 10 hours a week
Objective 1 Explore the history and mechanics of chatbots, enhancing your understanding of their design and function.
Objective 2 Construct and evaluate machine learning classifiers using BERT for effective text classification tasks.
Objective 3 Gain hands-on experience in creating and configuring functional chatbots using AWS Chatbot services.
Natural language processing
Amazon Web Services
Tensorflow
Machine Learning Methods
Applied Machine Learning
Artificial Intelligence and Machine Learning (AI/ML)
Application Development
Artificial Intelligence