Översikt

  • Lektioner

    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 book    
    		Lecture slides 
    updated
    		 Video   
    		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.)

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