Math Courses Before my MPhil:
- Calculus I,
- Calculus II,
- Linear Algebra and Analytic Topology,
- Discrete Mathematics (SS),
- Probability and Statistics,
- Complex Functions and Analysis (AD),
- Numerical Methods (SS),
- Mathematical Modeling (in MATLAB);
Engineering Courses Before my MPhil:
- Machinery Dynamics,
- Linear Systems and Control,
- Systems Dynamics,
- Thermofluid Mechanics,
- Artificial Intelligence in Products,
- Design for Product Safety and Reliability,
- Aircraft Systems,
- Discrete Time Signal and Systems,
- Engineering Electromagnetics I,
- Engineering Electromagnetics II,
- Communication Systems I (AD),
- Communication Systems II,
- Digital Signal Processing,
- Integrated Electronic Circuit Design,
- Mobile Robotics;
Computer Science Courses Before my MPhil:
- Introduction to Programming (in Python),
- C Programming Language,
- Introduction to Machine Vision,
- Data Structures (AD),
- Design and Analysis of Algorithms (AD),
- Theory Of Computation and Automata (SS)，
- Organization of Digital Computers,
- Introduction to Data Management (in SQL),
- Fundamentals of Parallel Computing,
- Computer Networks,
- Foundations of Artificial Intelligence,
- Advanced Algorithms;
My Undergraduate GPA: 3.83/4.00
Pure Math Courses During my MPhil:
- Stochastic Processes,
- Functional Analysis (SS, MA),
- Linear-System Theory (Half of this course is Matrix Analysis, the other half is Linear Systems Theory),
- Sensing, Estimation & Control,
- Convex Optimization, (AD)
- Game Theory (AD, MA),
- Advanced Probability Theory I (MA),
- Advanced Probability Theory II (MA),
- Statistical Machine Learning (MA),
- Statistical Reinforcement Learning (SS, CS 542),
- Foundations of Reinforcement Learning (SS, CS 6789),
- Advanced Topics in Deep Learning (SS, MA),
- Theory of Computation;
Non-pure-math Computer Science Courses During my MPhil:
- Operating Systems,
- Compilers (SS, CS 143),
- Software Engineering (SS, CSCI 3100),
- Computer System Security (SS, 6.858),
- Decision Making in Large Scale Systems (SS, 2.997),
- Design and Analysis of Algorithms,
- Deep Learning in Computer Vision (AD),
- Multi-Agent Systems (SS),
- Advanced Artificial Intelligence;
Marks
SS: self-study;
AD: auditing, which means I attended the class and all related activities as a normal student but earned no credits;
MA: this course is in the Department of Mathematics at HKUST;
OG: ongoing;
IP: in plan;
Aparecium
I Finished 40 credits officially in the first one and a half years of my MPhil.
Most courses I learned are based on my interests since a high GPA via carefully selected courses is not needed for a postgraduate researcher.
Some courses I learned are to boost my research, some are from my friends’ suggestions (usually my friends are also researchers).
A few courses I have to self-study via material from other universities, as there are no such related courses at HKUST.
I do not want to take any course in the last semester…Just DO RESEARCH!!!!⏰🏃♂️
By the way:
The Minimum Credit Requirement in HKUST is:
15 credits for MPhil in Robotics and Autonomous Systems
/ 12 credits for PhD in Physics
/ 15 credits for PhD in Electronic and Computer Engineering
/ 16 credits for MPhil in Computer Science and Engineering
/ 19 credits for PhD in Computer Science and Engineering
/ 24 credits for MPhil in Mathematics
/ 36 credits for PhD in Mathematics