Graduate

Courses

Computer Science

B659 Topics in Artificial Intelligence

Credits: 1-6

Prerequisite(s): Permission of instructor.

Special topics in artificial intelligence. May be repeated for a maximum of 12 credit hours.

Fall 2017


Instructor: Damir Cavar
Topic: Adv Natural Language Proccsng
Time: 1:00PM-2:15PM Tue, Thu
Location: Ballantine Hall, Room 209


Instructor: Damir Cavar
Topic: Semantics And Discourse
Time: 4:00PM-5:15PM Mon, Wed
Location: ACC102


Instructor: Michael Ryoo
Topic: Vision For Intellignt Robotics
Time: 4:00PM-5:15PM Tue, Thu
Location: Ballantine Hall, Room 233
Course URL (syllabus link or course homepage)


Instructor: Sandra Claudia Kuebler
Topic: Computatn & Linguistic Analys
Time: 2:30PM-3:45PM Mon, Wed
Location: Ballantine Hall, Room 307

Spring 2018


Instructor: Yuzhen Ye
Topic: Machine Learning Bioinformatic
Time: Multiple Times
Location: Multiple Locations


Instructor: Sandra Claudia Kuebler
Topic: Cross-linguistic Projection
Time: 1:00PM-2:15PM Tue, Thu
Location: Ballantine Hall, Room 240


Instructor: Damir Cavar
Topic: Applying Ml Techniques In Cl
Time: 9:30AM-10:45AM Tue, Thu
Location: Ballantine Hall, Room 118

  • Course History

      Spring 2017


      Instructor: Yuzhen Ye
      Topic: Machine Learning Bioinformatic
      Time: Multiple Times
      Location: Multiple Locations


      Instructor: Damir Cavar
      Topic: Applying Ml Techniques In Cl
      Time: 2:30PM-3:45PM Mon, Wed
      Location: Ballantine Hall, Room 118


      Instructor: Adam White
      Topic: Reinforcement Learning For Ai
      Time: 2:30PM-3:45PM Tue, Thu
      Location: Ballantine Hall, Room 330

      Fall 2016


      Instructor: Sriraam Natarajan
      Topic: Applied Machine Learning
      Time: 1:00PM-4:00PM Tue
      Location: HH2083


      Instructor: Damir Cavar
      Topic: Adv Natural Language Proccsng
      Time: 2:30PM-3:45PM Mon, Wed
      Location: Sycamore Hall, Room 103


      Instructor: Markus Dickinson
      Topic: Author Profiling
      Time: 2:30PM-3:45PM Tue, Thu
      Location: ACC107


      Instructor: Michael Ryoo
      Topic: Vision For Intellignt Robotics
      Time: 4:00PM-5:15PM Tue, Thu
      Location: Informatics West, Room 107
      Course URL (syllabus link or course homepage)


      Instructor: Sandra Claudia Kuebler
      Topic: Computatn & Linguistic Analys
      Time: 5:45PM-7:00PM Mon, Wed
      Location: Lindley Hall, Room 030


      Instructor: Donald Williamson
      Topic: Machine Perception & Audition
      Time: 4:00PM-5:15PM Mon, Wed
      Location: Lindley Hall, Room 008
      Course File (syllabus or course advertisement)
      Supplementary Description: This graduate-level seminar will review and discuss recent state-of-the-art algorithms that are designed to help machines better perceive and understand sound (speech and music). Topics will include deep neural networks, speech enhancement (separating speech from background noise), robust automatic speech recognition, speaker identification (verification and recognition), sound localization, and music processing.

      Spring 2016


      Instructor: Martha White
      Topic: Stochastic Optmztn For Ml
      Time: 2:30PM-5:00PM Mon
      Location: ACC112
      Course URL (syllabus link or course homepage)


      Instructor: Haixu Tang
      Topic: Machine Learning Bioinformatic
      Time: Multiple Times
      Location: Multiple Locations
      Course URL (syllabus link or course homepage)
      Supplementary Description: We aim to introduce a broad range of, from fundamantal and advanced, applications of bioinformatics methods and tools to solve problems in genomics and molecular biology. In this class, we will focus on how to apply them to solving biological problems in real life.This class will have a separate lab section, in which the students will be taught in how to solve biological problems in a step-by-step fashion.


      Instructor: Markus Dickinson
      Topic: Computatn & Linguistic Analys
      Time: 4:00PM-5:15PM Tue, Thu
      Location: Lindley Hall, Room 030


      Instructor: Sandra Claudia Kuebler
      Topic: Applying Ml Techniques In Cl
      Time: 1:00PM-2:15PM Mon, Wed
      Location: Student Building, Room 221


      Instructor: Adam White
      Topic: Reinforcement Learning For Ai
      Time: 4:00PM-5:15PM Tue, Thu
      Location: Swain Hall West, Room 103
      Course URL (syllabus link or course homepage)

      Fall 2015


      Instructor: Sriraam Natarajan
      Topic: Applied Machine Learning
      Time: 2:30PM-3:45PM Mon, Wed
      Location: PY111


      Instructor: Markus Dickinson
      Topic: Adv Natural Language Proccsng
      Time: 1:00PM-2:15PM Mon, Wed
      Location: Swain Hall East, Room 245


      Instructor: Sandra Claudia Kuebler
      Time: 1:00PM-2:15PM Tue, Thu
      Location: Student Building, Room 231


      Instructor: Christopher Raphael
      Topic: Music Infoprocessing: Audio
      Time: 9:30AM-10:45AM Mon, Wed
      Location: Lindley Hall, Room 008


      Instructor: Michael Ryoo
      Topic: Vision For Intellignt Robotics
      Time: 4:00PM-5:15PM Tue, Thu
      Location: Informatics West, Room 107
      Course URL (syllabus link or course homepage)
      Supplementary Description: In this graduate seminar course, we will review and discuss state-of-the-art computer vision methodologies while particularly focusing on their applications to robots (i.e., robot perception). Specific topics will include object recognition, activity recognition, deep learning for videos, and first-person vision for wearable devices and robots. The objective of the course is to understand important problems in computer vision and intelligent robotics, and discuss existing/future approaches.

      Spring 2015


      Instructor: Sandra Claudia Kuebler
      Topic: Computatn & Linguistic Analys
      Time: 2:30PM-3:45PM Tue, Thu
      Location: Lindley Hall, Room 030

      Spring 2015


      Instructor: David Crandall
      Topic: Image Processing & Recognition
      Time: Multiple Times
      Location: Informatics East, Room 130


      Instructor: Cenk Sahinalp
      Topic: Machine Learning Bioinformatic
      Time: Multiple Times
      Location: Multiple Locations


      Instructor: Sandra Claudia Kuebler
      Topic: Applying Ml Techniques In Cl
      Time: 2:30PM-3:45PM Mon, Wed
      Location: SY0006

      Fall 2014


      Instructor: Sriraam Natarajan
      Topic: Machine Learning
      Time: 11:15AM-12:30PM Tue, Thu
      Location: Ballantine Hall, Room 344


      Instructor: Sandra Claudia Kuebler
      Topic: Adv Natural Language Proccsng
      Time: 4:00PM-5:15PM Tue, Thu
      Location: PY115


      Instructor: Markus Dickinson
      Topic: Detecting Latent User Properties In Text
      Time: 4:00PM-5:15PM Mon, Wed
      Location: Ballantine Hall, Room 321

      Spring 2014


      Instructor: Haixu Tang
      Topic: Machine Learning Bioinformatic
      Time: Multiple Times
      Location: Multiple Locations
      Course URL (syllabus link or course homepage)
      Supplementary Description: We aim to introduce a broad range of applications of bioinformatics methods and tools to solve problems in genomics and molecular biology. Prior to this class, the students should have learned basic methods and theories in bioinformatics, e.g. by taking I519. In this class, we will focus on how to apply them to solving biological problems in real life. Some advanced computational techniques that in bioinformatics, e.g. Hidden Markov model (HMM) and Bayesian Network (BN).


      Instructor: Sriraam Natarajan
      Topic: Relational Probabilistic Model
      Time: 11:15AM-12:30PM Tue, Thu
      Location: Ballantine Hall, Room 345
      Course URL (syllabus link or course homepage)


      Instructor: Kris Hauser
      Topic: Robotics
      Time: 9:30AM-10:45AM Mon, Wed
      Location: Ballantine Hall, Room 242


      Instructor: Sandra Claudia Kuebler
      Topic: Parsng Morphologclly Rich Lang
      Time: 5:45PM-7:00PM Mon, Wed
      Location: MM401


      Instructor: Sandra Claudia Kuebler
      Topic: Computatn & Linguistic Analys
      Time: 11:15AM-12:30PM Mon, Wed
      Location: Student Building, Room 230

      Fall 2013


      Instructor: Sandra Claudia Kuebler
      Topic: Advanced Natural Language Processing
      Time: 4:00PM-5:15PM Tue, Thu
      Location: PH017

      Spring 2013


      Instructor: Yuzhen Ye
      Topic: Machine Learning Bioinformatic
      Time: Multiple Times
      Location: Multiple Locations


      Instructor: Kris Hauser
      Topic: Robotics
      Time: 1:00PM-2:15PM Mon, Wed
      Location: Woodburn Hall, Room 005

      Spring 2012


      Instructor: Haixu Tang
      Topic: Machine Learning Bioinformatic
      Time: Multiple Times
      Location: Multiple Locations


      Instructor: Sandra Claudia Kuebler
      Topic: Machine Learning - Computational Lingustics
      Time: 4:00PM-5:15PM Tue, Thu
      Location: Woodburn Hall, Room 005


      Instructor: Christopher Raphael
      Topic: Music Infoprocessing: Audio
      Time: 1:00PM-2:15PM Tue, Thu
      Location: Informatics West, Room 105

      Fall 2011


      Instructor: Esfandiar Haghverdi
      Topic: Information Theory & Inference
      Time: 2:30PM-3:45PM Tue, Thu
      Location: Lindley Hall, Room 008
      Course URL (syllabus link or course homepage)
      Supplementary Description: Description: This is a first course in Information Theory. I will try to cover the basics of information theory, for example as outlined in the first 10 chapters of the textbook below. However, my personal bias will be towards connections of the material we will be discussing to applications in statistical inference. The connections between information theory and statistics were observed and developed back in 1950s in the work of Kullback and Leibler, but there are several new applications of information theory in machine learning and other areas where inference plays a significant role. My basic plan for this course is to cover the basics of information theory first as I think this way the students will get almost all the material they need to tackle their own problems, however I will try to find time to discuss applications in statistics. I would like to end this brief description by a quote from the father of information theory, Claude E. Shannon (from IRE-Information Theory, 1956, page 3). Indeed, the hard core of information theory is, essentially, a branch of mathematics, a strictly deductive system. A thorough understanding of the mathematical foundation and its communication application is surely a prerequisite to other applications.

      Spring 2011


      Instructor: Kris Hauser
      Topic: Robot Motion
      Time: 11:15AM-12:30PM Tue, Thu
      Location: Informatics East, Room 122
      Course URL (syllabus link or course homepage)
      Supplementary Description: Intelligent agents need to coordinate many degrees-of-freedom under complex operational constraints to achieve future goals, to sense and react to disturbances in real-time, and to interact with human operators and other agents. This graduate seminar course covers frameworks, theories, and algorithms for motion planning and control, with applications to robots, humans, intelligent vehicles, virtual characters, biological molecules, and smart medical devices. Topics will include kinematic and dynamic modeling, motion planning, optimal control, Bayesian filtering, and Markov decision processes.


      Instructor: Haixu Tang
      Instructor: Sun Kim
      Topic: Machine Learning Bioinformatics
      Time: Multiple Times
      Location: Multiple Locations


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