Introduction to Computer...

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Introduction to Computer Science CSCI 109 Andrew Goodney Spring 2018 China – Tianhe-2 Lecture 9: Networks March 26th, 2018

Transcript of Introduction to Computer...

Page 1: Introduction to Computer Sciencebytes.usc.edu/files/cs109/sp18/slides/CSCI109-Lecture9-Networks.pdf · Takeaways u Networks allow computers to communicate (exchange data) u Data is

Introduction to Computer ScienceCSCI109

AndrewGoodneySpring2018

China– Tianhe-2

Lecture9:NetworksMarch26th,2018

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Schedule

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Midterm Scores

u 30questions,1pointeach

u Maxscore:30

u Minscore:8

u Mean:22.87

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Early Internet Drawings

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Early Internet Drawings

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January 1st, 1983

u ARPANet switchestoTCP/IP– stillinuse!

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Takeaways

u Networksallowcomputerstocommunicate(exchangedata)u Dataisbrokenintopackets(smallchunks)andsentoversharedcircuits

(packetswitching)byswitchesandroutersu Internetisanetwork-of-networks– Nooneowner/regulatoru TheInternetprovidesanAPIforaservice(networking)thatletsapp

developerswritesoftware(abstraction)u Servicesareimplementedwithprotocolsthatarestackedinlayers(more

abstraction)u TCP/IPisthecoreprotocolonwhichmostinternetworkingisbasedu SecuritywasnotanimportantdesignfeaturewhentheInternetwasinvented

– we’restillpayingforthisnowu Bandwidth,latency,throughputaretheimportantnetworkperformance

metricsu USC/ISIhasbeeninvolvedininventingthemodernInternetsince(notquite)

thebeginning(e.g DNS,IANA,ICANN)26


07: Random Variables II - Stanford Universityweb.stanford.edu/class/archive/cs/cs109/cs109.1208/lectures/07_rvs_ii.pdfLisa Yan, CS109, 2020 Consider an experiment: 𝑛independent

07: Random Variables II - Stanford Universityweb.stanford.edu/class/archive/cs/cs109/cs109.1208/lectures/07_rvs_ii.pdfLisa Yan, CS109, 2020 Consider an experiment: 𝑛independent

Introduction to Computer Sciencebytes.usc.edu/files/cs109/slides/CSCI109-Lecture9-Networks-New.pdf · 1 Mbps link. example: §1 Mb/s link §each user: • 100 kb/s when “active”

Introduction to Computer Sciencebytes.usc.edu/files/cs109/slides/CSCI109-Lecture9-Networks-New.pdf · 1 Mbps link. example: §1 Mb/s link §each user: • 100 kb/s when “active”

Cassandra Swett CE Specialist —Vegetable and Field Crop ... › sites › veg_crop_sjc › files › 319726.pdf(CS91) F. falciforme Isolate 2 (CS109) F. falciforme Isolate 3 (CS162)

Cassandra Swett CE Specialist —Vegetable and Field Crop ... › sites › veg_crop_sjc › files › 319726.pdf(CS91) F. falciforme Isolate 2 (CS109) F. falciforme Isolate 3 (CS162)

09: Continuous RVs - web.stanford.eduweb.stanford.edu/class/archive/cs/cs109/cs109.1208/lectures/09... · Earthquakes Major earthquakes (magnitude 8.0+) occur once every 500 years.*

09: Continuous RVs - web.stanford.eduweb.stanford.edu/class/archive/cs/cs109/cs109.1208/lectures/09... · Earthquakes Major earthquakes (magnitude 8.0+) occur once every 500 years.*

15: General Inferenceweb.stanford.edu/class/cs109/lectures/15_general_inference_blank.pdfLisa Yan, CS109, 2020 Quick slide reference 2 3 General Inference: intro 15a_inference 15 Bayesian

15: General Inferenceweb.stanford.edu/class/cs109/lectures/15_general_inference_blank.pdfLisa Yan, CS109, 2020 Quick slide reference 2 3 General Inference: intro 15a_inference 15 Bayesian

07: Variance, Bernoulli, Binomial - Stanford Universityweb.stanford.edu/class/cs109/lectures/07_bernoulli... · 2020. 10. 14. · Lisa Yan and Jerry Cain, CS109, 2020 Consider an

07: Variance, Bernoulli, Binomial - Stanford Universityweb.stanford.edu/class/cs109/lectures/07_bernoulli... · 2020. 10. 14. · Lisa Yan and Jerry Cain, CS109, 2020 Consider an

Introduction to Computer Sciencebytes.usc.edu/files/cs109/slides/CSCI109-Lecture1...Introduction to Computer Science CSCI 109 Andrew Goodney Fall 2019 Lecture 1: Introduction August

Introduction to Computer Sciencebytes.usc.edu/files/cs109/slides/CSCI109-Lecture1...Introduction to Computer Science CSCI 109 Andrew Goodney Fall 2019 Lecture 1: Introduction August

25: Logistic Regression...Lisa Yan, CS109, 2020 Quick slide reference 2 3 Background 25a_background 9 Logistic Regression 25b_logistic_regression 27 Training: The big picture 25c_lr_training

25: Logistic Regression...Lisa Yan, CS109, 2020 Quick slide reference 2 3 Background 25a_background 9 Logistic Regression 25b_logistic_regression 27 Training: The big picture 25c_lr_training

Python for Probabilityweb.stanford.edu/class/cs109/handouts/H03_python_slides.pdf · • High level, interpreted, and general-purpose dynamic programming language. PRO’s: • free,

Python for Probabilityweb.stanford.edu/class/cs109/handouts/H03_python_slides.pdf · • High level, interpreted, and general-purpose dynamic programming language. PRO’s: • free,

The Poisson distribution - web.stanford.eduweb.stanford.edu/class/archive/cs/cs109/cs109.1178/lectures/08-poisson.pdf · The Poisson distribution Will Monroe July 12, 2017 with materials

The Poisson distribution - web.stanford.eduweb.stanford.edu/class/archive/cs/cs109/cs109.1178/lectures/08-poisson.pdf · The Poisson distribution Will Monroe July 12, 2017 with materials

04: Conditional Probability and Bayesweb.stanford.edu › class › archive › cs › cs109 › cs109.1208 › ... · 31 Bayes’ Theorem II LIVE 61 Monty Hall Problem LIVE. Conditional

04: Conditional Probability and Bayesweb.stanford.edu › class › archive › cs › cs109 › cs109.1208 › ... · 31 Bayes’ Theorem II LIVE 61 Monty Hall Problem LIVE. Conditional

16: Continuous Joint Distributions - Stanford Universityweb.stanford.edu/class/archive/cs/cs109/cs109.1208/...hits at (456.2344132343, 532.1865739012)? The CS109 logo was created by

16: Continuous Joint Distributions - Stanford Universityweb.stanford.edu/class/archive/cs/cs109/cs109.1208/...hits at (456.2344132343, 532.1865739012)? The CS109 logo was created by

CS109: Probability for Computer Scientists”Probability is a number between 0 and 1” In-person, discussion-oriented lecture MWF 1:30pm PT (

CS109: Probability for Computer Scientists”Probability is a number between 0 and 1” In-person, discussion-oriented lecture MWF 1:30pm PT (

Introduction to - math.uaa.alaska.eduafkjm/cs109/handouts/dotnet.pdf · • This does not mean all languages are not case sensitive above the CLR! Some .NET ... MSIL Company Address

Introduction to - math.uaa.alaska.eduafkjm/cs109/handouts/dotnet.pdf · • This does not mean all languages are not case sensitive above the CLR! Some .NET ... MSIL Company Address

20: Maximum Likelihood Estimationweb.stanford.edu/.../20_mle_blank-announcements.pdf5. / = 12 +4 11 0 = 1 = Bernoulli model, parameter 0 = 0.2.! Lisa Yan, CS109, 2020 What are parameters?

20: Maximum Likelihood Estimationweb.stanford.edu/.../20_mle_blank-announcements.pdf5. / = 12 +4 11 0 = 1 = Bernoulli model, parameter 0 = 0.2.! Lisa Yan, CS109, 2020 What are parameters?

Lecture 18: Central Limit Theorem - Stanford Universityweb.stanford.edu/class/archive/cs/cs109/cs109.1188/lectures/18_clt.pdfLecture 18: Central Limit Theorem Lisa Yan August 6, 2018.

Lecture 18: Central Limit Theorem - Stanford Universityweb.stanford.edu/class/archive/cs/cs109/cs109.1188/lectures/18_clt.pdfLecture 18: Central Limit Theorem Lisa Yan August 6, 2018.

22: MAPweb.stanford.edu/class/cs109/lectures/22_map.pdfLisa Yan, CS109, 2020 MAP in practice •Flip a coin 8 times. Observe 6=7heads and 8=1tail. •What is the MAP estimator of the

22: MAPweb.stanford.edu/class/cs109/lectures/22_map.pdfLisa Yan, CS109, 2020 MAP in practice •Flip a coin 8 times. Observe 6=7heads and 8=1tail. •What is the MAP estimator of the

Introduction to Computer Sciencebytes.usc.edu/files/cs109/fa17/slides/CSCI109... · uCharacterize functions according to how fast they grow uThe growth rate of a function is called

Introduction to Computer Sciencebytes.usc.edu/files/cs109/fa17/slides/CSCI109... · uCharacterize functions according to how fast they grow uThe growth rate of a function is called

06 random variables - Stanford Universityweb.stanford.edu/class/cs109/lectures/06_random_variables.pdfLisa Yan, CS109, 2019 Probability of events 3 E or F *&∪, E and F *&, Just add!

06 random variables - Stanford Universityweb.stanford.edu/class/cs109/lectures/06_random_variables.pdfLisa Yan, CS109, 2019 Probability of events 3 E or F *&∪, E and F *&, Just add!

10: The Normal (Gaussian) Distribution€¦ · Lisa Yan, CS109, 2020 A Gaussian maximizes entropy for a given mean and variance. Part of CS109 learning goals: •Translate a problem

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