AI Microcredentials Foundational Technical Bundle
Build your technical base in AI through math, machine learning and analytics. Courses include Machine Learning Fundamentals, Essential Math Tools for AI and more. Gain confidence in data-driven decision-making.
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Overview
Unlock the power of data with Purdue’s Artificial Intelligence Microcredentials bundle
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. Students earn a certificate of completion and digital badge from Credly 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:
- Interpret data sets, trends and model outputs using analytical frameworks.
- Apply core math and statistics principles to machine learning problems.
- Translate algorithmic concepts into practical, real-world understanding.
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.
Explore Our 100% Online Courses
Customize your studies to fit your career goals
Lay the groundwork for your journey with Purdue’s core AI technical bundle. Strengthen your understanding of data analytics, essential math concepts and machine learning fundamentals. Through courses such as Machine Learning Fundamentals, Essential Math Tools for AI and Data Analytics for Decision Makers, you’ll gain the analytical and coding literacy to interpret algorithms, work with data and prepare for advanced AI applications.
Foundational – Technical Courses
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, eigen decomposition
– 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
| 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 Gradient Descent Convergence and Momentum Acceleration |
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:
Basic probability
Linear algebra
Ordinary differential equations (knowledge of ordinary differential equations will be valuable but is not required in order to be successful)
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
| 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 Convexity and Algorithms |
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
| 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: Jin Kocsis
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
| 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
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