ARIN7101 Statistics in Artificial Intelligence
Lecturer:
Office:
Tel No.:
E-mail:
Tutor:
Lecture Time:
Venue:
Dr. Chenyang Zhang
RR120
3917 8314
zhangcys@hku.hk
Mr. Chenyang Zhang
(RR209, Email: chyzhang@connect.hku.hk)
Mr. Jinhong Ni
(RR113 Email: jhni@connect.hku.hk)
Mr. Jingyi Lu
(RR201 Email: lujingyi@connect.hku.hk)
Thursday, 7:00 p.m. to 9:50 p.m.
CYCP1
Course Description
The development of artificial intelligence has revolutionized the theory and practice
of statistical learning, while novel statistical learning approaches are becoming an
integral part of artificial intelligence. By focusing on the interplay between statistical
learning and artificial intelligence, this course reviews the main concepts underpinning
classical statistical learning, studies computer-intensive methods for conducting statistical
learning, and examines important issues concerning statistical learning drawn upon
modern artificial intelligence technologies. Contents include classical frequentist and
Bayesian inferences, resampling methods, regularization, introduction on Markov
chain and Markov decision process.
Texts Used Lecture notes will be provided. No text is required while relevant
references are listed as follows:
Carlin, B. and Louis, T. (2008). Bayesian Methods for Data Analysis. Third
Edition. Chapman and Hall/CRC.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. and Rubin, D.
B. (2014). Bayesian Data Analysis. Third Edition. Chapman and Hall/CRC.
Givens, G.H. and Hoeting, J.A. (2005). Computational Statistics. Wiley, New
York.
Robert, C.P. and Casella, G. (2005). Monte Carlo Statistical Methods (2nd Ed.).
Springer, New York.
Koller, D. and Friedman, N. (2009). Probabilistic graphical models: principles and
techniques. MIT press.
Sutton, RS. and Barto, AG., (2018). Reinforcement learning: An introduction.
Second Edition. MIT press.
Puterman, ML. (2014). Markov decision processes: discrete stochastic dynamic
programming. John Wiley & Sons.
HKU ARIN7101 (2023-24, Semester 1)
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ARIN7101 Statistics in Artificial Intelligence
Learning Outcomes
Upon successful completion of the course, students should understand
1. Introduction on Bayesian Analysis
2. Approximation Inference and Variational Inference
3. Regularization and Model Selection
4. Large Scale Hypothesis Testing
5. Nonparametric Bayesian Analysis
6. Empirical Bayes
7. Bayesian Network
8. Markov Chain and Markov Decision Process
9. Dynamic Programming and Monte Carlo Methods
Most importantly, students should be able to apply them to solve practical
problems using Python.
Teaching and Assessment
One 2-hour lecture and one 1-hour tutorial are given per week. Students will
take 1 group project and 1 final exam. The final grade will be based on one 2-hour
written examination (60% weighting) and a coursework assessment (40% weighting)
based on assignments, tutorials and a group project. Partially or wholly copied
assignments will be penalized and/or reported as plagiarism. (See university website:
http://www.hku.hk/plagiarism)
Absence from Class Test
If for any reason you are or have been unable to attend a class test, and if you
wish to have a supplementary class test, you should write to the lecturer and the
General Office of the Department of Statistics and Actuarial Science giving reasons
for your absence within 5 days of the absence. A special/suppplementary test
is normally granted to those absent from the original test due to illness and with
original medical certificate provided. Students absent due to other reasons are not
granted a special/supplementary test unless with very special circumstances and
with valid documental proofs provided.
HKU ARIN7101 (2023-24, Semester 1)
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