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Lectures.Spring2018 History
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Classical inference – III, course review
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 [May 24] No class (ISET 2018)
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Classical inference – III, course review
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Classical inference – III, course review
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 The final exam is scheduled at 1:30 pm, June 11
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 [May 7] No class (national holiday)
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 [Apr. 19] Bernoulli process
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 [Apr. 19] Bernoulli process (makeup class)
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 [Apr. 23] Poisson process – I
 [Apr. 26]
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 [Apr. 23] (Class begins at noon) Poisson process – I
 [Apr. 26] No class (미래통신기술 워크샵)
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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
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 [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)
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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. 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
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 [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 E3506)
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 Assignment 2 (due: April 9. The drop box is in front of E3506)
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 Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E3506)
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 Assignment 2 (due: April 9: The drop box is in front of E3506)
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 Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E35 )
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 Assignment 2 (due: April 9: The drop box is in front of the CSI Lab., E3506)
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 [Apr. 5] Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
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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
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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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 [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
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 [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., E35 )
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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)
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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 (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/ )
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 [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
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 [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
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 [Mar. 15] Discrete random variables; probability mass functions; expectations
 [Mar. 19] Discrete random variable examples; joint PMFs
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 25. Classical inference – III, course review
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 25. Classical inference – III, course review
 Final exam
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 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
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 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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KyungJoon Park Office hours: By appointment, E3513 DGIST
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KyungJoon Park (kjp@, x6314, and E3513) Office hours: Right after class or by appointment
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Office hours: By appointment, E3513 DGIST
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Office hours: By appointment, E3513 DGIST
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MTh 13:3015:00 pm, E3113
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MTh 13:3015:00 pm, E3113
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Office hours: By appointment, E3513 DGIST
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Office hours: By appointment, E3513 DGIST
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 [February 26] Probability models and axioms
 2. Conditioning and Bayes rule
 3. Independence
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 [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 13:3015:00 pm, E3113
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Office hours: By appointment, 3309 DGIST
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Office hours: By appointment, E3513 DGIST
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 1. Probability models and axioms
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 [February 26] Probability models and axioms
