Intro273Awinter11

Intro273Awinter11 - Machine Learning ICS 273A Instructor...

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Unformatted text preview: Machine Learning ICS 273A Instructor: Max Welling What is Expected? • Class • Homework – Required, (answers will be provided) • A Project – See webpage • Quizzes – A quiz every Friday – Bring scantron form (buy in UCI bookstore) • Maybe a final • Programming in MATLAB. Syllabus • introduction: overview, examples, goals. • Classification I: decision trees, random forests, boosting, k-nearest neighbors, Naïve Bayes, over-fitting, bias variance trade-off, cross-validation. • Classification 2: neural networks: perceptron, logistic regression, multi-layer networks, back- propagation. • Classification 3: kernel methods & support vector machines. • Clustering & dimensionality reduction: (kernel) k-means, (kernel) PCA. • Kernel design • Nonlinear dimension reduction. • (Kernel) Fisher linear discriminant analysis • (Kernel) canonical correlation analysis • Algorithm evaluation, hypothesis testing. • week 9/10 : project presentations. Machine Learning according to •The ability of a machine to improve its performance based on previous results. •The process by which computer systems can be directed to improve their performance over time. •Subspecialty of artificial intelligence concerned with developing methods for software to learn from experience or extract knowledge from examples in a database. •The ability of a program to learn from experience — that is, to modify its execution on the basis of newly acquired information. •Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets. Machine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations. ... Machine Learning according to •The ability of a machine to improve its performance based on previous results. •The process by which computer systems can be directed to improve their performance over time. •Subspecialty of artificial intelligence concerned with developing methods for software to learn from experience or extract knowledge from examples in a database. •The ability of a program to learn from experience — that is, to modify its execution on the basis of newly acquired information....
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Intro273Awinter11 - Machine Learning ICS 273A Instructor...

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