PPT-Data Mining CSCI 307, Spring

Author : lauren | Published Date : 2022-06-18

2019 Lecture 17 Covering algorithms II 1 Covering Example continued 2 Age Spectacle prescription Astigmatism Tear production rate Recommended lenses Young Myope

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Data Mining CSCI 307, Spring: Transcript

2019 Lecture 17 Covering algorithms II 1 Covering Example continued 2 Age Spectacle prescription Astigmatism Tear production rate Recommended lenses Young Myope No Reduced None Young. . 8. Sorting. 1. CSCI 3333 Data Structures. Outline. Basic definitions. Sorting algorithms. Bubble sort. Insertion sort. Selection sort. Quick sort. Shell sort. Merge sort. Example programs. CSCI 3333 Data Structures. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Shannon Quinn. (with content graciously and viciously borrowed from William Cohen’s 10-605. Machine Learning with Big Data and Stanford’s MMDS MOOC . http://www.mmds.org/. ). “Big Data”. Astronomy. . 20. Hashing / Hash tables. 1. CSCI 3333 Data Structures. Outline. Basic definitions. Different hashing techniques. Linear probing. Quadratic probing. Separate chaining hashing. Comparing hashing with binary search trees. Shannon . Quinn. (with thanks to William Cohen of . Carnegie Mellon and . Jure . Leskovec. of Stanford). “Big Data”. Astronomy. Sloan Digital Sky Survey. New Mexico, 2000. 140TB over 10 years. Large Synoptic Survey Telescope. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). in Robotics Engineering. Blink . Sakulkueakulsuk. D. . Wilking. , and T. . Rofer. , . Realtime. Object Recognition . Using Decision . Tree . Learning, 2005. . http. ://. www.informatik.uni-bremen.de/kogrob/papers/rc05-objectrecognition.pd. Professor Tom . Fomby. Director. Richard B. Johnson Center for Economic Studies. Department of Economics. SMU. May 23, 2013. Big Data:. Many Observations on Many Variables . Data File. OBS No.. Target Var.. Core Methods in Educational Data Mining EDUC691 Spring 2019 Welcome! Administrative Stuff Is everyone signed up for class? If not, and you want to receive credit, please talk to me after class Class Schedule Lambda Calculus, Intro to Haskell. Announcements. Grades in Rainbow . Grades coming soon. Quizzes 1. -3. Homework 1-2. HW3 due on Thursday. HW5 will be out Thursday. Quiz 3. Spring . 20. . CSCI 4450/6450, A . http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am. Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.

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