SQL – Part 1 SQL and Data Definition Language What is SQL? SQL stands for Structured Query Language SQL was initially developed at IBM (SEQUEL → SQL), as one of the first commercial languages for the relational data model. 1986 – SQL was standardised by ANSI and ISO ( SQL-86). 1989 – SQL was revised […]
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1/81 Week 3 Workshop 2/81 Housekeeping 1 Thank you again for providing us with your valuable feedback! 2 Refer to the post in Wattle News Forum for makeup information for the CECS teaching pause. 3 Assessment on SQL (Assignment 1) will be available on Wattle at 11:59pm on Aug 20 (Friday) and due at 11:59pm
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PowerPoint Presentation Lecturer: Ben Rubinstein Lecture 17. Multi-armed bandits COMP90051 Statistical Machine Learning Copyright: University of Melbourne COMP90051 Statistical Machine Learning This lecture • Bandit setting vs Learning with experts ∗ Have to pick an expert (aka. arm) ∗ Observe rewards only for chosen arm • Aka. Sequential decision making under uncertainty ∗ Simplest explore-vs-exploit
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Entity-Relationship Model – Part 4 From ER to Relations Recap – Data Modeling Requirements ER diagram Relational database schema Relational DBMS Conceptual level Logical level Physical level ER design is subjective: There are many ways to model a given scenario. Analyzing alternative schemas is important. Constraints play an important role in designing a good database.
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Entity-Relationship Model – Part 1 Database Design Process IT Projects1 2011 2012 2013 2014 2015 SUCCESSFUL 29% 27% 31% 28% 29% CHALLENGED 49% 56% 50% 55% 52% FAILED 22% 17% 19% 17% 19% 1 CHAOS report by Standish Group, 2015 IT Projects1 2011 2012 2013 2014 2015 SUCCESSFUL 29% 27% 31% 28% 29% CHALLENGED 49%
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SQL – Part 3 Data Manipulation Language (Simple SQL Queries) Simple SQL Queries SQL provides the SELECT statement for retrieving data from a database. The SELECT statement has the following basic form: SELECT attribute_list FROM table_list [WHERE condition] [GROUP BY attribute_list [HAVING group_condition]] [ORDER BY attribute_list]; Note: Only SELECT and FROM are mandatory. The symbol
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Microsoft PowerPoint – 01 Lecturer: Ben Rubinstein Lecture 1. StatML Welcome and Maths Review COMP90051 Statistical Machine Learning Copyright: University of Melbourne COMP90051 Statistical Machine Learning This lecture • About COMP90051 • Review: Probability theory • Review: Linear algebra • Review: Sequences and limits 2 COMP90051 Statistical Machine Learning Subject objectives • Develop an appreciation for the role of statistical ML, advanced foundations and applications • Gain an understanding of a representative selection of ML techniques – how ML works • Be able to design, implement and evaluate ML systems • Become a discerning ML consumer 3 COMP90051 Statistical Machine Learning Subject content • The subject will cover topics from Foundations of statistical learning, linear models, non‐linear bases, regularised linear regression, generalisation theory, kernel methods, deep neural nets, multi‐armed bandits, Bayesian learning, probabilistic models • Theory in lectures; hands‐on experience with range of toolkits in workshop pracs and projects • vs COMP90049: much depth, much rigor, so wow 4 COMP90051 Statistical Machine Learning Subject staff / Contact hours 5 Contacting
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The Relational Model COMP 421 @ McGill 1 Database Models Copyright By PowCoder代写 加微信 powcoder • Theentity-relationshipisasemanticmodel(quite rich in its expressiveness) but it is not used as a model in any DBS. • Relationalmodel – Most common – Vendors: IBM DB2, Microsoft SQL Server, Oracle, SAP Hana, SAP Sybase, etc… – Open-source: PostgreSQL, MySQL, SQLite,
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Journal of Management Policies and Practices September 2014, Vol. 2, No. 3, pp. 71-82 ISSN: 2333-6048 (Print), 2333-6056 (Online) Copyright © The Author(s). 2014. All Rights Reserved. Published by American Research Institute for Policy Development DOI: 10.15640/jmpp.v2n3a4 URL: http://dx.doi.org/10.15640/jmpp.v2n3a4 Are Leadership and Management Different? A Review Dr. 1 Management and leadership have been used differently
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Levels of Abstraction Learning Outcomes · Understand the motivation for different programming paradigms: to abstract machine operation into human understandable and composable programs · Understand the difference between syntax the textual symbols and grammatical rules of a program, and semantics the meaning of what is computed · Understand that there are different models of computation upon which different programming