Back to overview over all lectures...
|Kuo Yuan-Chu||Chang Hui-Ting|
|office:||Engineering Building IV, Office 716A (ED716A)||Engineering Building IV, Office 716A (ED716A)|
|phone:||03-571 21 21 ext. 54630||03-571 21 21 ext. 54630|
|office hours:||on appointment||on appointment|
To make our and your life easier, let's agree on the following rule: You may contact or visit the TAs at any time also outside of office hours. However, if you haven't made an appointment in advance, they have the right to tell you that they haven't got time right at that moment.
The course is scheduled for 3 hours per week:
The course starts on Tuesday, 22 February, and finishes on Wednesday, 22 June.
NCTU requests that every teacher offers two hours per week where students may come to ask questions:
However, we would like to encourage you to show up in the teacher's or teaching assistant's office at any time in case you have questions about the class or related subjects. Moreover, we are always available during and after classes.
Dimitri P. Bertsekas, John N. Tsitsiklis: "Introduction to Probability," Athena Scientific, Massachusetts, 2002 (first edition) or 2008 (second edition).
For certain topics there will be additional handouts during class.
Every week, an exercise will be distributed in class and also made available online for download. This exercise will consist of several problems that need to be solved at home and handed in during the class of the following week. A model solution will be distributed in class and made available online afterwards.
We believe the exercises to be extremely important and crucial to the understanding of the course. They also serve as a preparation for the mid-term and final exams and we therefore highly recommend to solve them. To pass the course you need to hand in at least 10 exercises.
There will be one mid-term and one final exam. Both exams are going to last three hours and be open-book. Details about the covered material will be published in due time.
The grade will be an average of
The grade of the homework will not be based on the correctness of the answers, but rather on the effort the student shows in trying to solve them. Moreover, I will try to reward students who participate actively in the course.
This course is worth 3 credits.
The lecture will be held in English.
Note that some details of this program might change in the course of the semester.
Note that some linked documents in this table can only be downloaded from within NCTU and NTHU!
|W||Date||Topic||Handouts||Exercise (due on)||Solutions||Comments|
|1||22 Feb.||Introduction, set theory, probabilistic models||Syllabus (Version 2)||Exercise 1 (1 Mar.)|
|23 Feb.||Conditional probability, total probability theorem||
|2||1 Mar.||Total probability theorem, Bayes' rule, independence||Exercise 2 (8 Mar.)|
|2 Mar.||Independence, counting, discrete RV||Solutions 1|
|3||8 Mar.||Discrete RV: PMF||Exercise 3 (15 Mar.)|
|9 Mar.||Functions of RVs, expectations, mean, variance, joint PMF||Solutions 2|
|4||15 Mar.||Conditional PMF||Exercise 4 (22 Mar.)|
|16 Mar.||Conditional PMF, independent RVs; continuous RVs: PDF, CDF||Solutions 3|
|5||22 Mar.||Continuous RVs: PDF, CDF, Exponential RV||Exercise 5 (29 Mar.)|
|23 Mar.||Continuous RVs: Gaussian RV, conditioning||Solutions 4|
|6||29 Mar.||Continuous RVs: conditioning, joint PDF||Exercise 6 (12 Apr.)|
|30 Mar.||Continuous RVs: joint PDF, Bayes' rule, derived distributions||Solutions 5|
|7||5 Apr.||No lecture (Holiday)||
|6 Apr.||No lecture (Holiday)||
|8||12 Apr.||Continuous RVs: derived distributions||Handout 1||Exercise 7 (26 Apr.)|
|13 Apr.||Continuous RVs: derived distributions, transform, moment generating function||Solutions 6|
|9||19 Apr.||Moment generating function, conditional variance||
||Solutions 7 (short version)|
|20 Apr.||Mid-Term Exam||
||ATTENTION: This is a 3 hours exam: 10:10–13:00|
|10||26 Apr.||Discussion exam, sum of random number of independent RVs||Exercise 8 (3 May)|
|27 Apr.||Covariance and correlation, MMSEE, LMMSEE||Solutions 7|
|11||3 May||MMSEE, LMMSEE, covariance matrices||Exercise 9 (10 May)|
|4 May||Covariance matrices, multivariate Gaussian distribution||Solutions 8|
|12||10 May||Multivariate Gaussian distribution, stochastic processes, stationarity||Exercise 10 (17 May)|
|11 May||Stochastic processes, stationarity, Bernoulli process||Solutions 9|
|13||17 May||Poisson process||Exercise 11 (24 May)|
|18 May||Poisson process||Solutions 10|
|14||24 May||Markov process: definitions, classifications||Exercise 12 (31 May)|
|25 May||Markov process: classifications, steady state and stationarity||Solutions 11|
|15||31 May||Markov process: steady state and stationarity||Exercise 13 (7 Jun.)|
|1 Jun.||Markov process: steady state and stationarity||Solutions 12||Please fill out online class evaluation before 17 June!|
|16||7 Jun.||Markov process: long-term frequency interpretation, short-term transient behavior|| Exercise 14 (15 Jun.)
Exercise 15 (15 Jun.)
|8 Jun.||Markov process: short-term transient behavior, limit theorems: inequalities||Solutions 13|
|17||14 Jun.||Limit theorems: convergence, strong law of large numbers||
|15 Jun.||Limit theorems: convergence, strong law of large numbers, central limit theorem|| Solutions 14,
|18||21 Jun.||No lecture (moved to Friday)||
|22 Jun.||Final Exam||
||ATTENTION: This is a 3 hours exam: 10:00–13:00!|
|24 Jun.||Discussion of final exam and Coffee Time||We meet at 13:30 outside in the coffee-shop beside the information building|
-||- _|_ _|_ / __|__ Stefan M. Moser
[-] --__|__ /__\ /__ Senior Researcher & Lecturer, ETH Zurich, Switzerland
_|_ -- --|- _ / / Adj. Professor, National Chiao Tung University (NCTU), Taiwan
/ \  \| |_| / \/ Web: http://moser-isi.ethz.ch/