Computer Science 189

Title Introduction to Machine Learning
Units 4
Description Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Course Guide Course Guide
Sections Instructor Teaching Effectiveness How worthwhile was this course?
Spring 2016 Jonathan Shewchuk 6.7 / 7 6.7 / 7
Fall 2015 Alexei Efros 6.6 / 7 6.3 / 7
Isabelle Guyon 4.8 / 7 6.1 / 7
Spring 2015 Peter Bartlett 4.4 / 7 5.9 / 7
Alexei Efros 6.3 / 7 6.4 / 7
Spring 2014 Alexei Efros 6.1 / 7 6.1 / 7
Jitendra Malik 5.7 / 7 6.2 / 7
Spring 2013 Jitendra Malik 5.9 / 7 5.9 / 7
Fall 1990 Lotfi A. Zadeh 5.6 / 7 5.1 / 7
Fall 1989 Lotfi A. Zadeh 5.6 / 7 5.5 / 7
Fall 1988 Lotfi A. Zadeh 5.7 / 7 5.4 / 7
Overall Rating Teaching Effectiveness How worthwhile was this course?
5.8 / 7 5.9 / 7
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