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CS 478 - Machine Learning

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CS 478 - Machine Learning. 2. Limitations of AVL (I). Consider the MONK1 problem:6 attributesA1: 1, 2, 3A2: 1, 2, 3A3: 1, 2A4: 1, 2, 3A5: 1, 2, 3, 4A6: 1, 22 classes: 0, 1Target concept: If (A1=A2 or A5=1) then Class 1. CS 478 - Machine Learning. 3. Limitations of AVL (II). Can you build a

CS 478 - Machine Learning

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CS 9633 Machine Learning

CS 9633 Machine Learning. Computational Learning Theory. Adapted from notes by Tom Mitchell http://www-2.cs.cmu.edu/~tom/mlbook-chapter-slides.html. Theoretical Characterization of Learning Problems. Under what conditions is successful learning possible and impossible?

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CS 60050 Machine Learning

CS 60050 Machine Learning. What is Machine Learning?. Adapt to / learn from data To optimize a performance function Can be used to: Extract knowledge from data Learn tasks that are difficult to formalise Create software that improves over time. When to learn

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CS 446: Machine Learning

CS 446: Machine Learning. Dan Roth University of Illinois, Urbana-Champaign danr@illinois.edu http://L2R.cs.uiuc.edu/~danr 3322 SC. CS446: Machine Learning. Tuesday, Thursday: 9:30pm-10:45pm 1310 DCL Office hours: Tuesday, Thursday 10:45-11:30am [DCL]

578 views • 22 slides

CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. The Need for and Role of Bias. Learning. Rote: Until you discover the rule/concept(s), the very BEST you can ever expect to do is: Remember what you observed Guess on everything else Inductive:

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CS 478 - Machine Learning

CS 478 - Machine Learning. Projects Data Representation Basic testing and evaluation schemes. Programming Issues. Program in any platform you want

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CS 446: Machine Learning

CS 446: Machine Learning. Dan Roth University of Illinois, Urbana-Champaign danr@illinois.edu http://L2R.cs.uiuc.edu/~danr 3322 SC. A nnouncements. Class Registration: Still closed; stay tuned. My office hours: Tuesday, Thursday 10:45-11:30 E-mail; Piazza; Follow the Web site

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CS 6243 Machine Learning

CS 6243 Machine Learning. Advanced topic: pattern recognition (DNA motif finding). Final Project. Draft description available on course website More details will be posted soon Group size 2 to 4 acceptable, with higher expectation for larger teams Predict Protein-DNA binding.

656 views • 47 slides

CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. Clustering: Distance-based Approaches. What is Clustering?. Unsupervised learning. Seeks to organize data elements into “ reasonable ” groups. Typically based on some similarity (or distance) measure defined over data elements.

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CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. Data Manipulation (Adapted various sources, including G. Piatetsky-Shapiro, Biologically Inspired Intelligent Systems (Lecture 7) and R. Gutierrez-Osuna ’ s Lecture). Type Conversion.

799 views • 68 slides

CS 446: Machine Learning

CS 446: Machine Learning. Dan Roth University of Illinois, Urbana-Champaign danr@illinois.edu http://L2R.cs.uiuc.edu/~danr 3322 SC. CS446: Machine Learning. Tuesday, Thursday: 12:30pm-1:45pm 1320 DCL Office hours: Tuesday 1:45-3:30 pm [my office]

447 views • 26 slides

CS 446: Machine Learning

CS 446: Machine Learning. Dan Roth University of Illinois, Urbana-Champaign danr@illinois.edu http://L2R.cs.uiuc.edu/~danr 3322 SC. CS446: Policies. Info page Note also the Schedule Page and our Notes on the main page. Cheating No . We take it very seriously. Homework :

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CS 60050 Machine Learning

CS 60050 Machine Learning. 17 Jan 2008. CS 391L: Machine Learning: Decision Tree Learning. Raymond J. Mooney University of Texas at Austin. color. color. green. green. red. red. blue. blue. shape. shape. pos. C. neg. B. circle. circle. triangle. triangle. square.

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CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. Backpropagation. The Plague of Linear Separability. The good news is: Learn-Perceptron is guaranteed to converge to a correct assignment of weights if such an assignment exists The bad news is:

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CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. Linear and Logistic Regression (Adapted from various sources) (e.g., Luiz Pessoa PY 206 class at Brown University). Regression.

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CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. SVM. Maximal-Margin Classification (I). Consider a 2-class problem in R d As needed (and without loss of generality), relabel the classes to -1 and +1 Suppose we have a separating hyperplane Its equation is: w.x + b = 0

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CS 536: Machine Learning

CS 536: Machine Learning. Fall 2005 Ahmed Elgammal Dept of Computer Science Rutgers University. Outlines. Class policies What is machine learning Some basics. Machine Learning ?. What is machine learning (From Wikipedia).

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CS 782 Machine Learning

CS 782 Machine Learning. 1. Introduction. Prof. Gheorghe Tecuci. Learning Agents Laboratory Computer Science Department George Mason University. Overview. What is Artificial Intelligence. What is Machine Learning. History of Machine Learning. Demo: Disciple learning agent.

372 views • 32 slides

CS 478 - Machine Learning

CS 478 - Machine Learning. Genetic Algorithms (I). Darwin’s Origin of Species: Basic Principles (I). Individuals survive based on their ability to adapt to the pressures of their environment (i.e., their fitness)

374 views • 35 slides

CS 478 - Machine Learning

CS 478 - Machine Learning. Genetic Algorithms (II). Schema (I). A schema H is a string from the extended alphabet {0, 1, *}, where * stands for “don't-care” (i.e., a wild card) A schema represents or matches a number of strings:. There are 3 L schemata over strings of length L.

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CS 478 – Tools for Machine Learning and Data Mining

CS 478 – Tools for Machine Learning and Data Mining. Clustering Quality Evaluation. Quality Metrics. External metrics Computed from a comparison to actual clustering (or classification) labels. Internal metrics

137 views • 10 slides

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