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. 

Recommended Prior Knowledge: Please Note: Prior knowledge of linear algebra, probability and optimization is recommended to support success, but is not required.

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

Faculty: Stanley Chan

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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.  

Recommended Prior Knowledge:
Please Note: Prior knowledge is recommended to support success, but is not required.
Basic probability
Linear algebra
Ordinary Differential Equations

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 

Faculty: Philip E. Paré and Shreyas Sundaram  

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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.  

Recommended Prior Knowledge: Please Note: Prior knowledge of basic programming, fundamental math, data handling, and general problem solving skills is recommended to support success, but is not required.

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

Faculty: Jin Kocsis

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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.  

Recommended Prior Knowledge: 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

Faculty: John Springer  

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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.

Recommended Prior Knowledge: None

Faculty: Elsayed Issa

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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.

Recommended Prior Knowledge: Please Note: Prior knowledge of machine learning and python programming is recommended to support success, but is not required.

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

Faculty Name: Xiumin Diao  

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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.

Recommended Prior Knowledge: 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

Faculty: Jin Kocsis   

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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.  

Recommended Prior Knowledge: Please Note: Prior knowledge of introductory programming code is recommended to support success, but is not required.

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 

Faculty: Julia Rayz   

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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. 

Recommended Prior Knowledge: 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

Faculty: David Peterson 

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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. 

Recommended Prior Knowledge: 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

Faculty: Daniel Schiff

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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.  

Recommended Prior Knowledge: Please Note: Prior knowledge of political regulatory Processes in general is recommended to support success, but is not required.

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

Faculty: Dr. Swati Srivastava 

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