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Lectures.Spring2018 History

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Classical inference – III, course review

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  • [May 24]
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  • [May 24] No class (ISET 2018)
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May 15, 2018, at 11:06 AM EST by 125.143.67.201 -
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Classical inference – III, course review

  • Final exam
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Classical inference – III, course review

May 15, 2018, at 11:06 AM EST by 125.143.67.201 -
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  • [June 11] Final exam
May 15, 2018, at 11:06 AM EST by 125.143.67.201 -
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  • The final exam is scheduled at 1:30 pm, June 11
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  • [May 7]
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  • [May 7] No class (national holiday)
April 23, 2018, at 05:06 PM EST by 125.143.67.201 -
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  • [Apr. 19] Bernoulli process
to:
  • [Apr. 19] Bernoulli process (makeup class)
April 23, 2018, at 05:05 PM EST by 125.143.67.201 -
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  • [Apr. 23] Poisson process – I
  • [Apr. 26]
to:
  • [Apr. 23] (Class begins at noon) Poisson process – I
  • [Apr. 26] No class (미래통신기술 워크샵)
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  • [Apr. 26] No class
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  • [Apr. 26]
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  • [May 7] No class
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  • [May 7]
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  • [May 24] ISET 2018
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  • [May 24]
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  • 13. Bernoulli process
  • 14. Poisson process – I
  • 15. Poisson process – II
  • 16. Markov chains – I
  • 17. Markov chains – II
  • 18. Markov chains – III
  • 19. Weak law of large numbers
  • 20. Central limit theorem
  • 21. Bayesian statistical inference – I
  • 22. Bayesian statistical inference – II
  • 23. Classical inference – I
  • 24. Classical inference – II
  • 25. Classical inference – III, course review
to:
  • [Apr. 19] Bernoulli process
  • [Apr. 23] Poisson process – I
  • [Apr. 26] No class
  • [Apr. 30] Poisson process – II
  • [May 3] Markov chains – I
  • [May 7] No class
  • [May 10] Markov chains – II
  • [May 14] Markov chains – III
  • [May 17] Weak law of large numbers
  • [May 21] Central limit theorem
  • [May 24] ISET 2018
  • [May 28] Bayesian statistical inference – I
  • [May 31] Bayesian statistical inference – II
  • [June 4] Classical inference – I
  • [June 7] Classical inference – II

Classical inference – III, course review

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  • [Apr. 5] (Class begins at noon) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
to:
  • [Apr. 5] (Class begins at noon) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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  • [Apr. 5] (Class begins at ??? determined on Apr. 2) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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  • [Apr. 5] (Class begins at noon) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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  • [Apr. 9 & 12] No class (ACM/IEEE CPS Week 2018)
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  • [Apr. 9 & 12] No class (CPS Week 2018)
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  • [Apr. 2] (Class begins at 1 pm) Multiple continuous random variables; Continuous Bayes rule; derived distributions
  • [Apr. 5] (Class begins at ??? determined on Apr. 2) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
to:
  • [Apr. 2] (Class begins at 1 pm) Multiple continuous random variables; Continuous Bayes rule; derived distributions (regular + makeup class)
  • [Apr. 5] (Class begins at ??? determined on Apr. 2) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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  • Assignment 2 (due: April 9: The drop box is in front of E3-506)
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  • Assignment 2 (due: April 9. The drop box is in front of E3-506)
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  • Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E3-506)
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  • Assignment 2 (due: April 9: The drop box is in front of E3-506)
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  • Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E3-5 )
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  • Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E3-506)
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  • [Apr. 5] Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
to:
  • [Apr. 5] (Class begins at ??? determined on Apr. 2) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
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  • The midterm exam is scheduled at 1:30 pm, April 16
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  • [Apr. 5] Derived distributions; convolution; covariance and correlation
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  • [Apr. 5] Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
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  • 12. Iterated expectations; sum of a random number of random variables
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  • Midterm exam
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  • [Apr. 16] Midterm exam
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  • [Apr. 2] Multiple continuous random variables; Continuous Bayes rule; derived distributions
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  • [Apr. 2] (Class begins at 1 pm) Multiple continuous random variables; Continuous Bayes rule; derived distributions
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  • [Mar. 26] Continuous random variables
  • [Mar. 29] Multiple continuous random variables
  • 10. Continuous Bayes rule; derived distributions
  • 11. Derived distributions; convolution; covariance and correlation
to:
  • [Mar. 26] Continuous random variables; Multiple continuous random variables
  • [Mar. 29] No class
  • [Apr. 2] Multiple continuous random variables; Continuous Bayes rule; derived distributions
  • [Apr. 5] Derived distributions; convolution; covariance and correlation
  • [Apr. 9 & 12] No class (ACM/IEEE CPS Week 2018)
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  • Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E3-5 )
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  • Midterm exam
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  • Midterm exam
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  • 8. Continuous random variables
  • 9. Multiple continuous random variables
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  • [Mar. 26] Continuous random variables
  • [Mar. 29] Multiple continuous random variables
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  • [Mar. 19] ( Class begins at noon) Discrete random variable examples; joint PMFs & Multiple discrete random variables (regular + makeup class)
to:
  • [Mar. 19] (Class begins at noon) Discrete random variable examples; joint PMFs & Multiple discrete random variables (regular + makeup class)
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  • [Mar. 19] ( Class begins at noon) Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 19] ( Class begins at noon) Discrete random variable examples; joint PMFs & Multiple discrete random variables (regular + makeup class)
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  • [Mar. 19 ( Class begins at noon)] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 19] ( Class begins at noon) Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 19 ( Class begins at noon] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 19 ( Class begins at noon)] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 19] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 19 ( Class begins at noon] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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  • [Mar. 22] No class (http://www.ictkorea.org/main/ )
to:
  • [Mar. 22] No class (ICT Convergence Korea 2018)
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  • [Mar. 19] Discrete random variable examples; joint PMFs
  • 7. Multiple discrete random variables: expectations, conditioning, independence
to:
  • [Mar. 19] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
  • [Mar. 22] No class (http://www.ictkorea.org/main/ )
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  • (Attach:)IC566_Assignment1_2018.pdf | Assignment 1] (due: March 29)
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  • (Attach:)IC566_Assignment1_2018.pdf | Assignment 1] (due: March 29)

here

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  • 5. Discrete random variables; probability mass functions; expectations
  • 6. Discrete random variable examples; joint PMFs
to:
  • [Mar. 15] Discrete random variables; probability mass functions; expectations
  • [Mar. 19] Discrete random variable examples; joint PMFs
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  • 4. Counting
to:
  • [Mar. 12] Counting
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to:
  • Midterm exam
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  • 25. Classical inference – III, course review
to:
  • 25. Classical inference – III, course review
  • Final exam
Changed lines 38-49 from:
  • 5. Discrete random variables
  • 6. Continuous random variables
  • 7. Continuous Bayes rule
  • 8. Derived distributions; convolution; covariance and correlation
  • 9. Iterated expectations
  • 10. Bernoulli process
  • 11. Poisson process
  • 12. Markov chains
  • 13. Weak law of large numbers
  • 14. Central limit theorem
  • 15. Bayesian statistical inference
  • 16. Classical inference
to:
  • 5. Discrete random variables; probability mass functions; expectations
  • 6. Discrete random variable examples; joint PMFs
  • 7. Multiple discrete random variables: expectations, conditioning, independence
  • 8. Continuous random variables
  • 9. Multiple continuous random variables
  • 10. Continuous Bayes rule; derived distributions
  • 11. Derived distributions; convolution; covariance and correlation
  • 12. Iterated expectations; sum of a random number of random variables
  • 13. Bernoulli process
  • 14. Poisson process – I
  • 15. Poisson process – II
  • 16. Markov chains – I
  • 17. Markov chains – II
  • 18. Markov chains – III
  • 19. Weak law of large numbers
  • 20. Central limit theorem
  • 21. Bayesian statistical inference – I
  • 22. Bayesian statistical inference – II
  • 23. Classical inference – I
  • 24. Classical inference – II
  • 25. Classical inference – III, course review
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Kyung-Joon Park
Office hours: By appointment, E3-513 DGIST

to:

Kyung-Joon Park (kjp@, x6314, and E3-513)
Office hours: Right after class or by appointment

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Office hours: By appointment, E3-513 DGIST

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Office hours: By appointment, E3-513 DGIST

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MTh 13:30-15:00 pm, E3-113

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MTh 13:30-15:00 pm, E3-113

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Office hours: By appointment, E3-513 DGIST

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Office hours: By appointment, E3-513 DGIST

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  • [February 26] Probability models and axioms
  • 2. Conditioning and Bayes rule
  • 3. Independence
to:
  • [Feb. 26] Probability models and axioms
  • [Mar. 5] Conditioning and Bayes rule
  • [Mar. 8] Independence
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Class hour and classroom:

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Class hour and classroom: \\

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Class hour and classroom: MTh 13:30-15:00 pm, E3-113

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Office hours: By appointment, 3-309 DGIST

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Office hours: By appointment, E3-513 DGIST

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  • 1. Probability models and axioms
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  • [February 26] Probability models and axioms
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Page last modified on June 05, 2018, at 12:24 PM EST