Introduction to the theory, algorithms, and applications of machine learning (supervised, reinforcement, and unsupervised) from data: What is learning? Is learning feasible? How can it be done? How can it be done well? The course offers a mix of theory, technique, and application with additional selected topics chosen from Pattern Recognition, Decision Trees, Neural Networks, RBF's, Bayesian Learning, PAC Learning, Support Vector Machines, Gaussian processes, and Hidden Markov Models.
| Spring | Summer | Fall | ||
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| 2023 |
Machine Learning From Data (4c)
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| 2022 |
Machine Learning From Data (4c)
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| 2021 |
Machine Learning From Data (4c)
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Machine Learning From Data (4c)
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| 2020 |
Machine Learning From Data (4c)
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| 2019 |
Machine Learning From Data (4c)
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| 2018 |
Machine Learning From Data (4c)
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| 2017 |
Machine Learning From Data (3c)
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| 2016 |
Machine Learning From Data (3c)
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| 2015 |
Machine Learning From Data (3c)
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| 2014 |
Machine Learning (3c)
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| 2013 |
Machine Learning (3c)
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| 2012 |
Machine Learning (3c)
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| 2011 |
Machine Learning (3c)
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| 2010 |
Machine Learning (3c)
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| 2009 |
Machine Learning (3c)
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| 2008 |
Machine Learning (3c)
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| 2007 |
Machine Learning (3c)
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| 2006 | ||||
| 2005 | ||||
| 2004 | ||||
| 2003 | ||||
| 2002 |
Machine Learning (3c)
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| 2001 |
Machine Learning (3c)
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Machine Learning (3c)
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| 2000 | ||||
| 1999 | ||||
| 1998 | ||||