Teaching

Educational Resources

• Teaching a Large Language Model to Use Tools
  • One of the most exciting directions work in LLM is giving them the ability to use computational tools, such as calculators, scientific databases, and real-time web search.
  • There are several fully-featured frameworks for doing this, such as LangChain.
  • For instructional use, however, it is useful to provide students with a very small and simple code base that they can extend with new tools.
  • Thus my tiny framework, gpt_with_tools_v2 .
  • Although this code works somewhat with GPT-3.5, it works much better with GPT-4. You will need a paid OpenAI GPT API account.
• Mazes: Breadth-first, Depth-first, Best-first, and A* Search Demo
  • Download AMazer.jar
  • Run java -jar AMazer.jar
  • Download source zip file AMazer.zip
• N-Queens: Randomized Backtracking and Local Search Demo
  • Click here to run the Typescript program.
  • Download source from Gitlab.
• Sokoban Solution Visualizer
  • This is not a Sokoban solver, but rather a visualizer for Sokoban problems and solutions.
  • See the Help page of the program for details of file formats, options, and downloads.
  • Click here to run the Typescript program.
• Walksat
  • Satisifiability testing by local search.
  • Go to the Walksat home page.
• Schema
  • Schema is a language for concisely stating Boolean logic formulas by using a first-order lisp-based syntax.
  • Requires Common Lisp.
  • Go to the Schema home page.

Classes

• CSC 200 Undergraduate Problem Seminar, Spring 2017
• DSC 530 Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms, Fall 2016
• DSC 531 Practicum In Data-Eanabled Research Into Human Behavior And Its Cognitive and Neural Mechanisms, Spring 2016
• NiC Neuro-Inspired Computation Reading Group, Spring 2015
• CSC 242 Artificial Intelligence, Spring 2014
• CSC 282 Design and Analysis of Efficient Algorithms, Fall 2013
• CSC 280 Computer Models & Limitations, Spring 2012
• CSC 297 Pervasive Computing, Fall 2011
• CSC 244/444 Logical Foundations of AI, Fall 2010
• CSC 161 The Art of Programming, Spring 2010
• CS 244/444 Logical Methods in Artificial Intelligence, Fall 2009
• CS 190/290B Machines of Loving Grace, Spring 2008
• CS 577 Computational Models of Human Behavior, Autumn 2007
• CSE 473 Introduction to Artificial Intelligence, Spring 2006
• CSE 590W 590W Computers & Disabilities (with Richard Ladner), Spring 2006
• CSE 590A AI Seminar, Spring 2006
• CSE 573 Introduction to Artificial Intelligence, Autumn 2005
• CSE 590W Assistive Technology (with Richard Ladner), Autumn 2005
• CSE 590A Research in Artificial Intelligence, Autumn 2005
• CSE 326 Introduction to Data Structures & Algorithms, Winter 2005
• CSE 591HK Research in Behavior Recognition, Winter 2005
• CSE 590UC/AI Current Research in Ubiquitous Computing and AI, Winter 2005
• CSE 590MV Markovia (Reading Seminar in Probabilistic Models), Winter 2005
• CSE 590AI Current Research in Artificial Intelligence, Autumn 2004
• CSE 590MV Markovia (Reading Seminar in Probabilistic Models), Autumn 2004
• CSE P573 Professional Masters Program Introduction to Artificial Intelligence, Autumn 2004
• CSE 574 Modeling Human Intelligence (AI II), Winter 2004
• CSE 473 Introduction to Artificial Intelligence, Autumn 2003
• CSE 590HK Reading Seminar in Assisted Cognition, Spring 2003
• CSE 592 Applications of Artificial Intelligence (PMP), Winter 2003
• CSE 326 Data Structures, Autumn 2002
• CSE 590HK Reading Seminar in Assisted Cognition, Autumn 2002
• CSE 326 Data Structures, Winter 2002
• CSE 573, Artificial Intelligence I, Autumn 2001
• CSE 590 HK, Learning to Reasoning, Spring 2001
• CSE 326 Data Structures, Fall 2000

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