Details

Full details of this special topic course are available in syllabus here.  Also see description in course catalog.

Bulletin Description

This  will be an introductory AI course focused on solving practical problems using open data available freely for reuse. It will teach different methods for data processing, generating analysis and communicating insights to users. Further, we will consider how an agent should take decisions in the presence of uncertainty and incomplete information.  The course will cover AI sub-fields of representation, learning, reasoning and utilities. 

Prerequisites

Preparatory Mathematics (e.g., Linear Algebra, Discrete Mathematics) and Computer Science (e.g., Data Structures, Software Engineering) courses 

 Learning outcomes

As a result of successful participation in this course, undergraduate students will be able to:

L1: Identify patterns in problems around us that can be solved with better information / insights derived from data. Example: gap in information about demand and supply.

L2: Explain opportunities, issues related to data and tools: (a) data meant for reuse, i.e., open data (b) data quality, (c) data integration, (d) privacy concerns and bias with data, (e) experiment design,

L3: Explain, execute and create analytical methods to process data: (a) unstructured data, (b) semi-structured data, (c) structured data

L4: Explain AI methods in data analysis: (a) Learning methods, (b) Reasoning, (c) Representation and standardization – knowledge graphs/ ontology, (d) Preferences, (e) Handling Uncertainty

As a result of successful participation in this course, graduate students will be able to do all of the above, and:

L5: Evaluate gaps in analytical methods and create new ones to process data

L6: Explain data-driven insights to end-users with user-oriented interfaces, provide explanations for produced output to build trust. Using interactive interfaces, like visualizations and chatbots, explain how users will be able to interact with insights and build trust in AI.

L7: Explain research findings in open areas and critique their contributions

Related Courses at UoSC

• Prof. Pooyan Jamshidi - CSCE 580 (introductory AI), CSCE 585 (machine learning systems).

• Prof. Forest Agostinelli - CSCE 590 (Deep reinforcement learning and search)