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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 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
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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., E3-5 )
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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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Kyung-Joon Park Office hours: By appointment, E3-513 DGIST
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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
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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 13:30-15:00 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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