Jeffrey A. Bilmes: Teaching
Classes that I have taught/am teaching
- Fall 2020: Submodular Optimizations and Machine Learning (EE563).
- Spring 2020: Advanced Introduction to Machine Learning (EE511).
- Winter 2020: Introduction to Machine Learning (EE596A-PMP).
- Fall 2019: Information Theory I (EE514).
- Spring 2019: Introduction to Deep Neural Networks (EE596A-PMP)
- Winter 2019: Introduction to Machine Learning (EE596A-PMP)
- Winter 2019: Introduction to Machine Learning (EE596A-PMP)
- Fall 2018: Graduate introduction to digital signal processing (EE518)
- Spring 2018: Submodular Optimizations and Machine Learning (EE563).
- Winter 2018: Automatic Speech Recognition (EE516)
- Fall 2017: EE512 Advanced Inference in Graphical Models
- Fall 2016, Winter 2017, Spring 2017 Sabbatical (at Google and Simons Institute of Theoretical Computing, Berkeley)
- Spring 2016: Submodular Optimizations and Machine Learning (EE563).
- Winter 2016: Information Theory I (EE514).
- Fall 2015: Buyout
- Spring 2015: Automatic Speech Recognition (EE516)
- Winter 2015: Buyout
- Fall 2014: EE512 Advanced Inference in Graphical Models.
- Spring 2014: Submodular Functions, their applications and optimization
- Winter 2014: Information Theory II
- Fall 2013: Information Theory I.
- Spring 2012 Speech Processing and Recognition
- Winter 2012 Dynamic Graphical Models
- Fall 2012 Submodular Functions
- Spring 2012: Information Theory II
- Winter 2012: Information Theory I.
- Fall 2011: EE512 Advanced Inference in Graphical Models.
- Spring 2011: Submodular Functions, their Optimization and Applications.
- Winter 2011: EE235
- Fall 2010: Release
- Spring 2010: EE443, Design and Applications of DSP.
- Winter 2010: EE595A, Dynamic Graphical Models (HMMs, DBNs, CRFs, and beyond). First time course is offered.
- Fall 2009: EE512, Graphical Models
- Spring 2008: EE515 Information Theory - II
- Fall 2006: EE596/Stat 591 Modern topics in machine learning: Multiway classification, preferences, intransitivity
- Fall 2006: EE235 Signals and Systems
- Spring 2006: EE512 Graphical Models
- Winter 2006: EE511 Information Theory I
- Fall 2005: EE235 Signals and Systems
- Spring 2005: EE596 Artificial Intelligence for Electrical Engineers
- Winter 2005: EE516 Speech Recognition
- Fall 2004: EE235 Signals and Systems
- Spring 2004: EE596B Graphical Models
- Winter 2004: EE595A, Information Theory
- Spring 2003: EE235, Signals and Systems
- Spring 2003: EE516 Speech Recognition
- Fall 2002: EE518, Graduate introduction to digital signal processing
- Spring 2002: EE596 Pattern Recognition with Graphical Models.
- Winter 2001: EE595 Information Theory
- Fall 2001: EE518, Graduate introduction to digital signal processing
- Spring 2001: Along with Marina Meila-Predoviciu from the statistics department, an advanced seminar on information theory and statistics entitled Information Theory, Statistics and Machine Learning.
- Winter 2001: EE516, Computer Speech processing and recognition.
- Fall 2000: EE518, Graduate introduction to digital signal processing
- Spring 2000: EE596 Introduction to graphical models for pattern recognition.
- Winter 2000: EE595 Information Theory
Regular Seminars that I run:
Machine Translation Reading Group
Probabilistic Reasoning with Graphical Models With Marina Meila.
In the past, I also ran my speech, language, and machine learning reading seminar series