COM502: Introduction

COM502: Machine Learning is a centerpiece of the SIAI's MBA AI/BigData (AI/Finance) program, together with COM503: Deep Learning and COM504: Reinforcement Learning.

The course assumes prior knowledge in regression analysis in following courses along with important understanding of key concepts in computational efficiency (discussed in COM501: Scientific Programming)

• STA501: Data-based Decision Making
• STA502: Math & Stat for MBA I
• STA503: Math & Stat for MBA II

The course initially covers regression analysis in a sense where traditional linear regression is most powerful. As discussed in earlier math & stat courses, conditional normal distribution on regression is the key for regression being the best linear unbiased estimator (BLUE).

From that point on, the discussion goes to classifiers like logistic regression and move onto graph models such as data grouping, ensembles, together with principal components. In the later part of the course, it covers factor analysis and introduce artificial neural network model as an extension of factor analysis in the context of graph models.

Though final term paper does cover a case example of Machine Learning to a real world application, most extensions are discussed in BUS501: AI in Digital Marketing, a business case course taught in parallel.

Course topics

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Course schedule

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Class discussion

The school strongly encourages in-class discussion, which covers 10% of the final grading. Students are given a discussion forum in all Moodle course pages, and various types of participations will be added to the final mark.

Plz note that only productive discussion activities will be positively marked. If any abusing is found, the student or the group of students will lose all 10% mark.

Final Examination

There will be a take-home final exam with 48 hours, which is usually provided on a friday 2 weeks after the last class.

Refer the sample exam questions for more details.

Grading and evaluation

Final marks will be broken down as below.

• 80%: Final examination
• 10%: Problem set submission (not graded - submission w/ reasonable amonnt work is sufficient)
• 10%: Forum discussion

For business track students, instead of final examination and problem sets, there will be an essay requirement covering 90% of the grading.

Essays are graded based on following criteria.

• Critical thinking
• Effective use of in-class knowledge
• Problem detection and solving in data scientific cases

All essays must not exceed 3,000 words.

Language

All contents must be submitted in English, although the school does not refrain students relying on AI tools for translation.