ELEC2844: Probabilistic Systems Analysis

The University of Hong Kong, Second Semester, 2022–23

Lecture Schedule and Materials

Links to lecture slides and homework will be posted as we progress in the course.

Month	Monday (1hr)	Thursday (2hrs)	Notes	Homework
JAN	16	19	Lecture 1: Fundamentals of Probability
	23	26	23, 26: Chinese New Year
	30	2
FEB	6	9	Lecture 2: Discrete Random Variables	hw1 (due Feb 28) hw1 solution
	13	16
	20	23	Lecture 3: Continuous Random Variables
	27	2
MAR	6	9	6, 9: reading week
	13	16	16: University holiday	hw2 (due Apr 6) hw2 solution
	20	23	Intermission Lecture 4: Several Random Variables
	27	30
APR	3	6	3: No class; You are encouraged to attend HKU President's Forum (see below) Lecture 5: Parameter Estimation I: Classical
	10	13	10: Easter Monday	hw3 (due Apr 30) hw3 solution
	17	20	Lecture 6: Parameter Estimation II: Bayesian
	24	27

Slides as Handouts

Lecture 1: handout1.pdf
Lecture 2: handout2.pdf
Lecture 3: handout3.pdf
Lecture 4: handout4.pdf
Lecture 5: handout5.pdf
Lecture 6: handout6.pdf

Tutorial Schedule and Materials

From the TAs: We will have weekly tutorials every Tuesday, from mid-February to end of April, from 9:30am – 10:30am. Venue: CPD-1.24 (CPD-LG map, campus map, classroom list)

Tutorial 1: February 14
Tutorial 2: February 21
Tutorial 3: February 28
Tutorial 4: March 14
Tutorial 5: March 21
Tutorial 6: March 28 (homework 1)
Tutorial 7: April 4
Tutorial 8: April 11
Tutorial 9: April 18 (homework 2)
Tutorial 10: April 25

Special Event

HKU President’s Forum – Distinguished Talk by Professor John Hennessy

Topic: Energy and Silicon Efficient Computing: a 45 Year Journey

Time: 2:30p.m. – 4:00p.m. on April 3 (Monday)

Venue: Room 218 (Convocation Room), Main Building, The University of Hong Kong

Audience: around 90-100

Abstract: For nearly four decades, computers have dramatically and relentlessly improved cost-performance. In the past 10+ years, that improvement has begun to abate limitations (the slowdown in Moore's Law and the end of Dennard scaling) and the increasing reliance by computer architects on speculative techniques to achieve higher performance. This slowdown comes just as the breakthroughs in machine learning are demanding massive increases in computing performance. A new direction based on domain-specific architectures is the only clear way forward.

EMS links:

For HKU members (HKU Portal login is required):

For non-HKU members: