MS-A0503 - First Course in Probability and Statistics, Lecture, 9.1.2023-22.2.2023
This course space end date is set to 22.02.2023 Search Courses: MS-A0503
Topic outline
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The first lecture is on Monday 9.1. at 10.15-12.00 in the U2 hall.
Note on lecture videos: The videos have two or more video streams: camera, computer screen, (possibly second screen). If you are watching videos in Panopto, you can switch between streams or maximize one stream to full screen. (Lecture 4B is using two screens.)Update on lecture content: We will not be covering Chebyshev's inequality on this course (on lectures or exercises), and it is not mandatory learning (will not be on exam). For those who are interested, you can find Chebyshev in the Lecture 2B slides and in the old L2B video, approx between 0:47:00 and 1:03:00 (16 minutes).- All lectures are on campus. There are 12 lectures: six Mondays at 10-12, and six Fridays at 10-12. All lectures are in U2, except 27.1. which is in hall B.
- Attending the lectures is optional, but mastering their content is mandatory.
- All lectures will be video recorded (unless technical trouble), and the videos will be available here. Old Zoom videos from spring 2022 are also available.
- Lecture slides are updated here as the course progresses (see the "Lecture slides updated" column). Until updated, slides from spring 2022 are available.
- Please read the textbook independently. Consult the table below to find relevant sections of the textbook. Note that Ross's book has very small coverage of Bayesian inference, which is an important modern approach. Read/listen lectures 5A & 5B to learn its basics.
- On some lectures there will be live demonstrations with Matlab or R code. Students on this course are not required to know or learn Matlab or R, but some lecture codes will be available for studying and experimenting for those who are interested (see lower on this page).
Lecture plan Lecture Topic Chapters in
Ross's bookLecture slides
updatedVideo Other 1A Probability: Concept and basic rules 3 8.1. video old video 1B Random variables and distributions 4.1 - 4.3 12.1. video old video 2A Expected value and transformations 4.4 - 4.5 16.1. video old video; Lecture codes 2B Standard deviation and correlation 4.6-4.7, 4.9 20.1. video old video; Lecture codes (households) 3A Distributions of sums and averages 5.1, 5.5, 6.1-6.3 23.1. video old video; Lecture codes (households, exponentials) 3B Statistical datasets 1, 2 video old video; Lecture codes (empirical distributions) 4A Parameter estimation 7.1-7.2 video old video; Lecture codes (maximizing likelihood) 4B Confidence intervals 7.3, 7.5 video old video 5A Bayesian inference 7.8, 14.3.4 6.2. video old video; Lecture codes (coins, star brightness) 5B More Bayesian inference 7.8, 14.3.4 10.2. video old video 6A Hypothesis testing 8.1-8.3, 8.6 13.2. video old video 6B Various topics - video old video part1, part2, part3, part4, part5;
Lecture codes (Monte Carlo, multinomials etc.)