CS 322 Week 2 As professional SE, always think about complexity reduction Stream data processing: big data using diff alg/data struc space O(1) Role of.

Slides:



Advertisements
Similar presentations
Computer Science and Engineering Laboratory, Transport-triggered processors Jani Boutellier Computer Science and Engineering Laboratory This.
Advertisements

Biointelligence Laboratory, Seoul National University
Computer science is a field of study that deals with solving a variety of problems by using computers. To solve a given problem by using computers, you.
Artificial Intelligence Lecture 2 Dr. Bo Yuan, Professor Department of Computer Science and Engineering Shanghai Jiaotong University
Partially-Observable Markov Decision Processes Tom Dietterich MCAI
Lecture 5: Learning models using EM
CS 547: Sensing and Planning in Robotics Gaurav S. Sukhatme Computer Science Robotic Embedded Systems Laboratory University of Southern California
Programme in Statistics (Courses and Contents). Elementary Probability and Statistics (I) 3(2+1)Stat. 101 College of Science, Computer Science, Education.
EMGT 501 HW # (b) (c) 6.1-4, Due Day: Sep. 21.
AppxA_01fig_PChem.jpg Complex Numbers i. AppxA_02fig_PChem.jpg Complex Conjugate.
Automated Changes of Problem Representation Eugene Fink LTI Retreat 2007.
1 Software engineering development process: the meiotic model Vito Veneziano.
CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Dr. Daniel Tauritz.
CS Bayesian Learning1 Bayesian Learning. CS Bayesian Learning2 States, causes, hypotheses. Observations, effect, data. We need to reconcile.
CS & IT Careers Where do you want to end up?. What Computer Science Isn't Digital Literacy / Using computer applications.
Tennessee Technological University1 The Scientific Importance of Big Data Xia Li Tennessee Technological University.
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 2.
Artificial Intelligence
Optimal predictions in everyday cognition Tom Griffiths Josh Tenenbaum Brown University MIT Predicting the future Optimality and Bayesian inference Results.
1 Performance Analysis of Coexisting Secondary Users in Heterogeneous Cognitive Radio Network Xiaohua Li Dept. of Electrical & Computer Engineering State.
Computer Science: A Structured Programming Approach Using C1 6-9 Recursion In general, programmers use two approaches to writing repetitive algorithms.
Probabilistic Robotics Bayes Filter Implementations.
M Machine Learning F# and Accord.net. Alena Dzenisenka Software architect at Luxoft Poland Member of F# Software Foundation Board of Trustees Researcher.
Introduction This module about Process Optimization was produced under the Program for North American Mobility in Higher Education as part of the Process.
CS 782 – Machine Learning Lecture 4 Linear Models for Classification  Probabilistic generative models  Probabilistic discriminative models.
Estimating Component Availability by Dempster-Shafer Belief Networks Estimating Component Availability by Dempster-Shafer Belief Networks Lan Guo Lane.
1 EECS 6083 Compiler Theory Based on slides from text web site: Copyright 2003, Keith D. Cooper, Ken Kennedy & Linda Torczon, all rights reserved.
Sparse Bayesian Learning for Efficient Visual Tracking O. Williams, A. Blake & R. Cipolloa PAMI, Aug Presented by Yuting Qi Machine Learning Reading.
Syllabus. We covered Regression in Applied Stats. We will review Regression and cover Time Series and Principle Components Analysis. Reference Book.
Optimal Component Analysis Optimal Linear Representations of Images for Object Recognition X. Liu, A. Srivastava, and Kyle Gallivan, “Optimal linear representations.
CS558 Project Local SVM Classification based on triangulation (on the plane) Glenn Fung.
M Machine Learning F# and Accord.net.
Operations Research The OR Process. What is OR? It is a Process It assists Decision Makers It has a set of Tools It is applicable in many Situations.
Introduction to Operations Research. MATH Mathematical Modeling 2 Introduction to Operations Research Operations research/management science –Winston:
Time-Space Trust in Networks Shunan Ma, Jingsha He and Yuqiang Zhang 1 College of Computer Science and Technology 2 School of Software Engineering.
Lecture Note 2 – Calculus and Probability Shuaiqiang Wang Department of CS & IS University of Jyväskylä
New inclusion functions in interval global optimization of engineering structures Andrzej Pownuk Chair of Theoretical Mechanics Faculty of Civil Engineering.
CS Statistical Machine learning Lecture 12 Yuan (Alan) Qi Purdue CS Oct
Professional Development of Software Engineers First day summary.
Mining of Massive Datasets Edited based on Leskovec’s from
Incremental Reduced Support Vector Machines Yuh-Jye Lee, Hung-Yi Lo and Su-Yun Huang National Taiwan University of Science and Technology and Institute.
Density Estimation in R Ha Le and Nikolaos Sarafianos COSC 7362 – Advanced Machine Learning Professor: Dr. Christoph F. Eick 1.
Some statistical musings Naomi Altman Penn State 2015 Dagstuhl Workshop.
Department of Electrical and Computer Engineering ABET Outcomes - Definition Skills students have graduation.
ICS 3UI - Introduction to Computer Science
Today.
Turing Machines Space bounds Reductions Complexity classes
Where do you want to end up?
MBI 630: Systems Analysis and Design
Probabilistic Models for Linear Regression
Decomposition.
Introduction This module about Process Optimization was produced under the Program for North American Mobility in Higher Education as part of the Process.
Blind Signal Separation using Principal Components Analysis
Probabilistic Models with Latent Variables
Logistic Regression & Parallel SGD
Upgrading Condor Best Practices
Unscented Kalman Filter
CS 322 week 1 summary Syllabus-grouping-grading, grading policy – fairness - NO cheating, gap-based classification/SVM CS: problem solving by computers.
Lecture 0: Introduction
CS 322 week 3 Gold standard of science: testing (randomized programming and testing, one major component of software engineering). Sample stat reasoning.
Programme Educational Objectives (PEOs)
Discrete Mathematics in the Real World
Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, Previously summarized by Yung-Kyun Noh Updated.
TECHNOLOGY, ENGINEERING AND DATA CONTINUING AND PROFESSIONAL EDUCATION
CS621: Artificial Intelligence Lecture 22-23: Sigmoid neuron, Backpropagation (Lecture 20 and 21 taken by Anup on Graphical Models) Pushpak Bhattacharyya.
Applied Statistics and Probability for Engineers
Algorithms Lecture # 26 Dr. Sohail Aslam.
Uncertainty Propagation
Logistic Regression Geoff Hulten.
CS 322 week 1 summary Syllabus-grouping-grading, grading policy – fairness - NO cheating, gap-based classification CS: problem solving by computers with.
Presentation transcript:

CS 322 Week 2 As professional SE, always think about complexity reduction Stream data processing: big data using diff alg/data struc space O(1) Role of math/stats (linear alg: eigen vector/value—google pagerank, calculus: Taylor series: Machine learning: gradient descent/save training sample, probability, Bayesian stats/subjective/belief-classic/frequentist, keep updating belief based on new evidence, information: -log(p), optimization/calculus: nullifying 1st derivative etc.) in problem modeling and solving in software engineering

CS 322 week 2 Mathematical/complexity modeling: objective function and optimization behind un/robustness of mean and median; problem analysis, objective function formulation; calculus + stats reasoning to justify Gold standard of science: testing (randomized testing, one major component of software engineering). Stat reasoning typical for algorithm design and problem solving Sw measurements: efficiency (space+time complexity), user-friendly: to ordinary user, understandability: to peer programmers (kolmogorov)