CSCE 580: Introduction to Artificial Intelligence
Quick Info - When and Where
Tuesday/Thursday 4:25 pm – 5:40 pm
In person at 300 Main St. | Room B213. Recordings to be available on Blackboard.
Catalog Information
CRN: CRN20329
Duration: 08/24/2023 - 12/18/2023
Instructor Information
Instructor: Biplav Srivastava
E-mail: biplav.s AT sc.edu
Office Hours: 1-2pm(M), 3-4pm (T); other times by appointment
GitHub for slides, sample code.
Attendance Policy
Students are expected to attend lectures and also go through class videos after the lecture. They are expected to participate in quizzes, do their project and complete paper reading.
Suggested Reading
Material from my earlier teachings AI and NLP courses: CSCE 590s: Data to Decisions (Spring 2021), Trusted AI (Fall 2021), and CSCE 771: Computer Processing of Natural Languages (Fall 2020, Fall 2022).
Articles and papers as announced in class.
Research Interests
See ongoing research activities in this area at my "Trusted AI" page.
Want to Learn about Artificial Intelligence for the Real World?
In recent years, Artificial Intelligence (AI) has generated wild excitement due its potential to transform businesses and societies around the world with technologies like ChatGPT, Dall-E, self-driving cars, Google Maps, Machine Translators, and Shazam, and yet, it has evoked tremendous fear and doomsday warnings about possible negative impacts like increasing unemployment, exacerbating injustices, data breaches and circumventing humans oversight. How does one learn AI to use them in the real world while also steering around practical issues? This course will introduce AI and also guide towards its safe usage for real-world applications.
Prerequisites
Experience with a first course in data structures (CSCE 350), programming (CSCE 330)
Learning outcomes
L1: Appreciate and work with diversity of data– text, speech and visual; focus will be, however, structured data (e.g., tables) and text (NLP; English)
L2: Learn techniques to derive insights from data spanning reasoning (e.g., symbolic) and learning (e.g., neural) in a decision-making setup
L3: Learn methods to represent and organize insights
L4: Make insights usable with people in a collaborative setting (“chatbots”)
L5: Understand issues related to usage of AI methods/ tools with people.
L6: Gain experience by build a real-work AI
Topics Covered
Week 1: Introduction, Aim: Chatbot / Intelligence Agent
Weeks 2-3: Data: Formats, Representation and the Trust Problem
Week 4-5: Search, Heuristics - Decision Making
Week 6: Constraints, Optimization – Decision Making
Week 7: Classical Machine Learning – Decision Making, Explanation
Week 8: Machine Learning - Classification
Week 9: Machine Learning - Classification – Trust Issues and Mitigation Methods
Topic 10: Learning neural network, deep learning, Adversarial attacks
Week 11: Large Language Models – Representation, Issues
Topic 12: Markov Decision Processes, Hidden Markov models - Decision making
Topic 13: Planning, Reinforcement Learning – Sequential decision making
Week 14: AI for Real World: Tools, Emerging Standards and Laws; Safe AI/ Chatbots
