Artificial Intelligence (AI) Microcredentials
Purdue’s AI Microcredentials Program offers quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. With an average completion time of only 15 hours, this program is an ideal upskilling opportunity for professionals who want to advance in their careers or learn new skills quickly.
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Overview
Unlock the power of data with Purdue’s Artificial Intelligence Microcredentials program
Purdue University’s Artificial Intelligence Microcredentials offer quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. Every course functions as its own mini-credential, and students earn a certificate for every course they complete. Students can pick and choose what course to take and stack credentials in topics that interest them.
Courses are also taught by the well-renowned faculty of Purdue. AI is revolutionizing hundreds of industries, and AI skills are some of the most in-demand job skills in today’s tech-driven market. Learn essential AI skills including:
- Understanding the current applications of AI and where the field is heading.
- How to apply AI technologies to solve real-world problems and contribute to organizational innovation.
- How to amass a robust AI skillset and build marketable expertise in emergent technologies.
The cost of attending Purdue varies depending on where you choose to live, enrollment in a specific program or college, food and travel expenses, and other variables. The Office of the Bursar website shows estimated costs for the current aid year for students by semester and academic year. These amounts are used in determining a student’s estimated eligibility for financial aid. You can also use our tuition calculator to estimate tuition costs.
Program Specifics
Course Description: This course provides a foundation for understanding machine learning and its applications by taking a learn-by-doing approach. Students will learn about machine learning by training a regression model to perform data analysis.
Prerequisites: None
Learning Outcome:
– Review of linear algebra: To review basic concepts in linear algebra including norm, inner product, linear combination, basis functions, matrix-vector multiplication, eigendecomposition
– Principles of regression: To understand the basic principles of regression, including loss function, linear models, parameter update
– Solving a regression problem: To derive the linear least squares solutions and understand the properties under-determined and over-determined linear systems
– Regularization techniques: To apply ridge regression techniques and LASSO regression techniques
– Optimization: To review constrained and unconstrained minimization, Lagrange multiplier, convexity, gradient descent, and stochastic gradient descent
Inner products and norms
Matrix Calculus Eigenvalues and eigenvectors
Principal Component Analysis Eigenface Problem
How does regression work?
Solving Linear Regression and Matrix-vector form of linear regression
Robust Linear Regression
Solving the Robust Regression Problem
LASSO Regularization
LASSO for Overfitting
Simple Constrained Optimization
| Module | Topic & Readings |
| Module 1 | Why do we need linear algebra in machine learning? Inner products and norms Matrix Calculus Eigenvalues and eigenvectors Principal Component Analysis Eigenface Problem |
| Module 2 | What is regression? How does regression work? Solving Linear Regression and Matrix-vector form of linear regression |
| Module 3 | Overdetermined and undetermined lease squares Robust Linear Regression Solving the Robust Regression Problem |
| Module 4 | Ridge Regression and Implementation LASSO Regularization LASSO for Overfitting |
| Module 5 | Convexity Lagrange and Multiplier Solving Simple Constrained Optimization |
Faculty: Stanley Chan
Course Description: Students will work through real-world problems and examples to understand the mathematical background for AI. By breaking concepts down and putting them in context, this course makes the math behind AI accessible for a wider audience.
Prerequisites: None
Learning Outcome:
– Analyze equations involving matrices by applying algebraic concepts such as rank, nullspace, linear independence, and eigenvalues
– Define properties of linear systems, including controllability, observability, and stability, and apply them to design state estimators and feedback controllers
– Define probability distributions and moments of random variables, and characterize the long-term behavior of stochastic processes
– Specify the fundamental optimality conditions for optimization problems, and implement basic algorithms to find the optimizers
System of Equations and Eigenvalues
Diagonalization and Definite Matrices
Norms
Search Algorithms and Trees
Shortest Paths
Controllability and Observability
Lyapunov Theory
Random Variables and Expectation
Markov Chains
Infimum and Supremum
| Module | Topic & Readings |
| Module 1 | Vectors and Matrices System of Equations and Eigenvalues Diagonalization and Definite Matrices Norms |
| Module 2 | Basics and Graph Properties Search Algorithms and Trees Shortest Paths |
| Module 3 | Basics and Stability Controllability and Observability Lyapunov Theory |
| Module 4 | Basics and Conditional Probability Random Variables and Expectation Markov Chains |
| Module 5 | Extrema and Optimality Infimum and Supremum |
Faculty: Philip E. Paré and Shreyas Sundaram
Course Description: This course provides in-depth conceptual explanation of supervised and unsupervised machine learning algorithms and how to implement them to address real-world problems.
Prerequisites: None
Learning Outcome:
– Assess and understand the core principles, applications, and limitations of machine learning, distinguishing its role from traditional programming
– Analyze a variety of supervised and unsupervised machine learning algorithms using prominent frameworks
– Integrate machine learning knowledge to address real-world challenges by choosing appropriate algorithms and techniques
Understand and Build Machine Learning Models
Logistic and Linear Regression
Applications of Clustering
| Module | Topic & Readings |
| Module 1 | Intro to Machine Learning Understand and Build Machine Learning Models |
| Module 2 | Supervised Machine Learning Algorithms Logistic and Linear Regression |
| Module 3 | Understand Machine Learning Algorithms Applications of Clustering |
| Module 4 | Capstone Project |
Faculty: Rishikesh P Fulari
Course Description: In this course, students will be able to explore data mining hands-on by using data mining tools for pattern recognition, visualization, artificial intelligence and more.
Prerequisites: None
Learning Outcome:
– Examine foundational concepts in data mining
– Differentiate between descriptive and predictive elements of data mining
– Contrast the strengths and weaknesses of supervised and unsupervised methods
Data Concepts
Data Quality
Pattern Recognition
Visualization and Large-Scale Data
Supervised Machine Learning
Deep Learning
| Module | Topic & Readings |
| Module 1 | Foundations of Data Mining Data Concepts Data Quality |
| Module 2 | Components of Data Mining Pattern Recognition Visualization and Large-Scale Data |
| Module 3 | Methods for Data Mining Supervised Machine Learning Deep Learning |
Faculty: John Springer
Course Description: Explore the emerging field of prompt engineering with AI tools like large language models (LLMs) (ChatGPT, Claude, Gemini, etc.) and image models (Midjourney, DALL-E, etc.) and learn to harness the power of Generative AI to improve daily life, work, and learning experiences. This asynchronous, online course offers a practical introduction to AI and LLMs through interactive content, short videos, practical exercises, and self-assessments. This course will help non-technical learners to understand AI, use AI tools effectively, and craft prompts that enhance AI’s utility in various applications. By engaging with this course, learners will gain skills to “talk” to AI tools, effectively becoming programmers of their digital interactions.
Prerequisites: None
| Module | Topic & Readings |
| Module 1 | Introduction to AI and Language Models |
| Module 2 | Interacting with Language Models |
| Module 3 | Crafting Effective Prompts |
| Module 4 | Practical Applications of Prompt Engineering |
Faculty: Elsayed Issa
Course Description: Machine Learning can be deployed in manufacturing to significantly increase production efficiency and capacity. In
this course, step-by-step tutorials on how to apply machine learning to analyze manufacturing data are presented. Students will learn how to create artificial intelligence solutions for manufacturing analytics.
Prerequisites: None
Learning Outcome:
– Explain the benefits of machine learning in manufacturing
– Describe the common operations in developing machine learning applications
– Apply machine learning for manufacturing analytics
Artificial Intelligence
Applications, Benefits, and Challenges
Load and Process Manufacturing Data
Work on Linear Regression Models
Compare Various Regression Models
Perform Feature Selection on Manufacturing Data
Build, Train, and evaluate a CNN Model
Augment Dataset
| Module | Topic & Readings |
| Module 1 | Smart Manufacturing Artificial Intelligence Applications, Benefits, and Challenges |
| Module 2 | Vehicle MPG Prediction Load and Process Manufacturing Data Work on Linear Regression Models |
| Module 3 | Used Car Price Prediction Compare Various Regression Models Perform Feature Selection on Manufacturing Data |
| Module 4 | Quality Inspection of Casting Products Build, Train, and evaluate a CNN Model Augment Dataset |
Faculty Name: Xiumin Diao
Course Description: This course will introduce the fundamental knowledge of machine learning techniques via a series of hands-on
real-world examples in Python. The overall aim is to provide the students with a good understanding of machine-learning technologies, building machine learning with Python, and applying machine-learning technologies to address real-world problems.
Prerequisites: None
Learning Outcome:
– Explain the relationship (main mechanisms, internal logic, computing components, and the usage constraints) of 8 machine learning models (Linear Regression, Logistic Regression, Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Autoencoder, General Adversarial Network (GAN), and Reinforcement Learning (RL))
– Program the basic realization of the machine learning models, stated in Learning Objective 1, in Python
– Apply the eight machine learning models stated in Learning Objective 1 to solve real-world problems
What is the Insight of Machine Learning
Linear Regression Implementation
Solving the Robust Regression Problem
Fully Connected Neural Network Implementation
Convolutional Neural Network Implementation
Recurrent Neural Network Implementation
Generative Adversarial Network Application and Implementation
Deep Reinforcement Learning Models Application and Implementation
| Module | Topic & Readings |
| Module 1 | What is Machine Learning What is the Insight of Machine Learning |
| Module 2 | Linear Regression Application Linear Regression Implementation |
| Module 3 | Logistic Regression Formulation and Implementation Solving the Robust Regression Problem |
| Module 4 | Introduction of Neural Networks Application Fully Connected Neural Network Implementation |
| Module 5 | Convolutional Neural Network Application Convolutional Neural Network Implementation |
| Module 6 | Recurrent Neural Network Application Recurrent Neural Network Implementation |
| Module 7 | Auto-Encoder Application and Implementation Generative Adversarial Network Application and Implementation Deep Reinforcement Learning Models Application and Implementation |
Faculty: Jin Kocsis
Course Description: This course focuses on the real-world uses of natural language processing systems, including the current capabilities of natural language processing systems and how NLP can be refined and improved.
Prerequisites: None
Learning Outcome:
– Describe the capabilities of existing NLP systems
– Analyze the gap that exists between a stated scenario and the existing capabilities of NLP systems
– Test solutions by measuring improvements introduced by NLP systems
Words vs Concepts and Explicit vs Implicit
General Domain and Specific Domain
Contextual Embeddings
LM Capabilities
Factual Correctness and Reasoning
Intro into Prompt Learning and Engineering
| Module | Topic & Readings |
| Module 1 | History of Natural Language Processing Words vs Concepts and Explicit vs Implicit General Domain and Specific Domain |
| Module 2 | Shallow NNs Contextual Embeddings LM Capabilities |
| Module 3 | Testing in General Factual Correctness and Reasoning Intro into Prompt Learning and Engineering |
| Module 4 | Capstone Reflection Project |
Faculty: Julia Rayz
Course Description: This course provides students with the real-world knowledge they need to navigate the risks of AI and how it’s changing the technology landscape. Students will break AI down into engaging, accessible concepts and explore the ethics of AI through real-world examples.
Prerequisites: None
Learning Outcome:
– Explain the history of AI research and why deep learning became the dominant approach
– Explain, in a non-technical way, how deep learning works
– Outline various risks of AI, including both speculative dangers and more tangible and immediate risks to the economy, the labor market, and civil society
| Module | Topic & Readings |
| Module 1 | History of Artificial Intelligence |
| Module 2 | A Crash Course on Deep Learning |
| Module 3 | The Singularity and Other Speculative Fears |
| Module 4 | The Immediate Risks of Artificial Intelligence |
Faculty: David Peterson
Course Description: This course explores the ethical and regulatory framework that underpin AI. Students will analyze real-world policy and governance strategies that seek to manage AI’s impacts and engage in debates that will shape the future of the field.
Prerequisites: None
Learning Outcome:
– Identify core concepts in the emerging AI policy domain including key actors, institutions, and governance strategies that have been proposed by or adopted in governments, firms, and civil society organizations
– Analyze and evaluate the social and ethical dimensions of AI, focusing on issue spotting and understanding the policy implications of these issues
– Examine current regulatory trends and policy frameworks for AI governance, including prominent debates and challenges
– Synthesize emerging best practices, issues, and debates in AI policy and governance, while developing strategies for continual monitoring and staying informed
| Module | Topic & Readings |
| Module 1 | Introduction to AI |
| Module 2 | Key Concepts in International Governance |
| Module 3 | Corporate Governance and Self-Regulation |
| Module 4 | Ethics and Impact Assessment |
| Module 5 | Getting Engaged and Building Expertise |
Faculty: Daniel Schiff
Course Description: This course covers the growing global demands for AI regulations and puts them in context so students can understand what risks these regulations seek to address and how companies and governments can anticipate and comply with them.
Prerequisites: None
Learning Outcome:
– Analyze the demand for global regulation of AI
– Identify key issues for AI regulation to address in a global context
– Explain leading hard and soft models for global AI regulation
| Module | Topic & Readings |
| Module 1 | Demand for Global AI Regulation |
| Module 2 | Core Issues for Global Regulation for AI |
| Module 3 | Hard Regulation: The European Model |
| Module 4 | Soft Regulation: Industry Standards |
Faculty: Dr. Swati Srivastava
Course Description: In this course, students will learn how to create engaging data stories by contradicting common perception. They will use data analysis and AI prompts using Chat GPT to produce effective data stories and justify why AI makes these data stories more effective.
Prerequisites: None
Learning Outcome:
– Identify how data stories should surprise, provide a new, more convincing explanation for time-worn ideas
– Identify ways to tell memorable, teachable arguments in the form of stories
– Examine how AI tools may be used to satisfy these conditions of storytelling
| Module | Topic & Readings |
| Module 1 | Data Storytelling Basics |
| Module 2 | Building for Data Stories |
| Module 3 | Data Analysis as Storytelling |
| Module 4 | Visualization |
| Module 5 | Composing a Data Story |
| Module 6 | Data Storytelling as an Ethical Choice |
| Module 7 | Designing Stories for Specific Audiences |
Faculty: Sorin Matei
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