CSCI B659 - Topics Artificial Intelligence
Fall 2023
- Instructor: Damir Cavar (dcavar)
- Sections: 6976
- Topic: Adv Natural Language Proccsng
- Instructor: Francis Tyers (ftyers)
- Sections: 10081
- Topic: Computatn & Linguistic Analys
- Instructor: Damir Cavar (dcavar)
- Sections: 12560
- Topic: Knowledge Graphs, Nlp, And Lan
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34263
- Topic: Applying Ml Tech To Nlp
Spring 2023
- Instructor: Raj Acharya (racharya)
- Sections: 36884
Spring 2023
- Instructor: Roni Khardon (rkhardon)
- Sections: 10219
- Instructor: Yijie Wang (yijwang)
- Sections: 4950,4951
- Topic: Machine Learning Bioinformatic
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 6530
- Topic: Applying Ml Techniques In Cl
Fall 2022
- Instructor: Francis Tyers (ftyers)
- Sections: 7322
- Topic: Adv Natural Language Proccsng
- Instructor: Francis Tyers (ftyers)
- Sections: 10874
- Topic: Computatn & Linguistic Analys
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 14343
- Topic: Sentiment Analysis
Spring 2022
- Instructor: Roni Khardon (rkhardon)
- Sections: 11213
- Instructor: Yijie Wang (yijwang)
- Sections: 5230,5231
- Topic: Machine Learning Bioinformatic
- Instructor: Zeeshan Sayyed (zasayyed)
- Sections: 6964
- Topic: Applying Ml Techniques In Cl
- Instructor: David Leake (leake)
- Sections: 12974
- Topic: Explainable Ai
Fall 2021
- Instructor: Nils Hjortnaes (nhjortn)
- Sections: 16779
- Topic: Adv Natural Language Proccsng
- Instructor: He Zhou (hzh1)
- Sections: 20917
- Topic: Computatn & Linguistic Analys
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 44752
- Topic: Multilingual Parsing
Spring 2021
- Instructor: Roni Khardon (rkhardon)
- Sections: 13426
- Topic: Lrn Theory & Graphical Models
- Instructor: Yijie Wang (yijwang)
- Sections: 5713,5714
- Topic: Machine Learning Bioinformatic
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 7680
- Topic: Applying Ml Techniques In Cl
- Instructor: Xuhong Zhang (zhangxuh)
- Sections: 33913
- Topic: Medical Image Analysis
- Supplementary Description: This course is a seminar in medical image analysis and the topics will be focused on deep learning models but other machine learning models will also be included. Seniors who already taken the undergraduate algorithm courses and with more than one year programming experience are also welcome to enroll for this seminar.
Fall 2020
- Instructor: Damir Cavar (dcavar)
- Sections: 8435
- Topic: Adv Natural Language Proccsng
- Instructor: Francis Tyers (ftyers)
- Sections: 13427
- Topic: Computatn & Linguistic Analys
- Instructor: Zoran Tiganj (ztiganj)
- Sections: 32188
- Topic: Cognitively Inspired Ai
- Instructor: Beth Plale (plale)
- Sections: 36493
- Topic: Intro To Ai And Infrastructure
- Instructor: Xiaozhong Liu (liu237)
- Sections: 36495
- Topic: Big Data Analytics
Spring 2020
- Instructor: Roni Khardon (rkhardon)
- Sections: 33923
- Topic: Reinforcement Learning For Ai
- Instructor: Yijie Wang (yijwang)
- Sections: 6112,6113
- Topic: Machine Learning Bioinformatic
- Instructor: Damir Cavar (dcavar)
- Sections: 8333
- Topic: Applying Ml Techniques In Cl
Fall 2019
- Instructor: Donald Williamson (williads)
- Sections: 14328
- Topic: Deep Learning Speech Processng
- Instructor: Damir Cavar (dcavar)
- Sections: 9062
- Topic: Adv Natural Language Proccsng
- Instructor: Francis Tyers (ftyers)
- Sections: 31723
- Topic: Computatn & Linguistic Analys
- Instructor: Saul Blanco (sblancor)
- Sections: 37197
- Topic: Combinatorics And Computing
- Instructor: Yijie Wang (yijwang)
- Sections: 37990
- Topic: Topics In Deep Learning
Spring 2019
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 12264
- Topic: Cross-linguistic Projection
- Instructor: Yuzhen Ye (yye)
- Sections: 6491,6492
- Topic: Machine Learning Bioinformatic
- Instructor: Damir Cavar (dcavar)
- Sections: 8982
- Topic: Applying Ml Techniques In Cl
- Instructor: Roni Khardon (rkhardon)
- Sections: 32837
- Topic: Lrn Theory & Graphical Models
Fall 2018
- Instructor: Donald Williamson (williads)
- Sections: 36113
- Topic: Deep Learning Speech Processng
- Instructor: Damir Cavar (dcavar)
- Sections: 9997
- Topic: Adv Natural Language Proccsng
Spring 2018
- Instructor: Yuzhen Ye (yye)
- Sections: 8065,8066
- Topic: Machine Learning Bioinformatic
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 31114
- Topic: Cross-linguistic Projection
- Instructor: Damir Cavar (dcavar)
- Sections: 10965
- Topic: Applying Ml Techniques In Cl
Fall 2017
- Instructor: Damir Cavar (dcavar)
- Sections: 10728
- Topic: Adv Natural Language Proccsng
- Instructor: Damir Cavar (dcavar)
- Sections: 10761
- Topic: Semantics And Discourse
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 13082
- Topic: Computatn & Linguistic Analys
Spring 2017
- Instructor: Yuzhen Ye (yye)
- Sections: 11891,11892
- Topic: Machine Learning Bioinformatic
- Instructor: Damir Cavar (dcavar)
- Sections: 15363
- Topic: Applying Ml Techniques In Cl
- Instructor: Adam White (adamw)
- Sections: 16961
- Topic: Reinforcement Learning For Ai
Fall 2016
- Instructor: Sriraam Natarajan (natarasr)
- Sections: 13268
- Topic: Applied Machine Learning
- Instructor: Damir Cavar (dcavar)
- Sections: 13348
- Topic: Adv Natural Language Proccsng
- Instructor: Markus Dickinson (md7)
- Sections: 13392
- Topic: Author Profiling
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 32156
- Topic: Computatn & Linguistic Analys
- Instructor: Donald Williamson (williads)
- Sections: 36136
- Topic: Machine Perception & Audition
- 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.
- Ad/Syllabus: https://homes.luddy.indiana.edu/classes/fall2016/csci/b659-williads/advertisement.pdf
Spring 2016
- Instructor: Haixu Tang (hatang)
- Sections: 11228,11229
- Topic: Machine Learning Bioinformatic
- 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.
- Homepage: https://homes.luddy.indiana.edu/classes/spring2016/csci/b659-hatang
- Instructor: Markus Dickinson (md7)
- Sections: 16128
- Topic: Computatn & Linguistic Analys
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 16153
- Topic: Applying Ml Techniques In Cl
Fall 2015
- Instructor: Sriraam Natarajan (natarasr)
- Sections: 15348
- Topic: Applied Machine Learning
- Instructor: Markus Dickinson (md7)
- Sections: 15489
- Topic: Adv Natural Language Proccsng
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 15564
- Instructor: Christopher Raphael (craphael)
- Sections: 36260
- Topic: Music Infoprocessing: Audio
- Instructor: Michael Ryoo (mryoo)
- Sections: 36371
- Topic: Vision For Intellignt Robotics
- 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.
- Homepage: https://homes.luddy.indiana.edu/classes/fall2015/csci/b659-mryoo
Spring 2015
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34281
- Topic: Computatn & Linguistic Analys
Spring 2015
- Instructor: David Crandall (djcran)
- Sections: 33351,33352
- Topic: Image Processing & Recognition
- Instructor: Cenk Sahinalp (cenksahi)
- Sections: 24955,24956
- Topic: Machine Learning Bioinformatic
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34753
- Topic: Applying Ml Techniques In Cl
Fall 2014
- Instructor: Sriraam Natarajan (natarasr)
- Sections: 33922
- Topic: Machine Learning
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34600
- Topic: Adv Natural Language Proccsng
- Instructor: Markus Dickinson (md7)
- Sections: 34791
- Topic: Detecting Latent User Properties In Text
Spring 2014
- Instructor: Haixu Tang (hatang)
- Sections: 25728,25729
- Topic: Machine Learning Bioinformatic
- 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).
- Homepage: https://homes.luddy.indiana.edu/classes/spring2014/csci/b659-hatang
- Instructor: Kris Hauser (hauserk)
- Sections: 27152
- Topic: Robotics
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34558
- Topic: Parsng Morphologclly Rich Lang
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34570
- Topic: Computatn & Linguistic Analys
Fall 2013
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 33810
- Topic: Advanced Natural Language Processing
Spring 2013
- Instructor: Yuzhen Ye (yye)
- Sections: 26737,26738
- Topic: Machine Learning Bioinformatic
- Instructor: Kris Hauser (hauserk)
- Sections: 30020
- Topic: Robotics
Spring 2012
- Instructor: Haixu Tang (hatang)
- Sections: 27816,27817
- Topic: Machine Learning Bioinformatic
- Instructor: Sandra Claudia Kuebler (skuebler)
- Sections: 34467
- Topic: Machine Learning - Computational Lingustics
- Instructor: Christopher Raphael (craphael)
- Sections: 34651
- Topic: Music Infoprocessing: Audio
Fall 2011
- Instructor: Esfandiar Haghverdi (ehaghver)
- Sections: 28860
- Topic: Information Theory & Inference
- 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.
- Homepage: https://homes.luddy.indiana.edu/classes/fall2011/csci/b659-ehaghver
Spring 2011
- Instructor: Kris Hauser (hauserk)
- Sections: 16547
- Topic: Robot Motion
- 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.
- Homepage: https://homes.luddy.indiana.edu/classes/spring2011/csci/b659-hauserk
- Instructor: Haixu Tang (hatang)
- Sections: 31507,31508
- Topic: Machine Learning Bioinformatics