Hong Kong Baptist University

MACHINE LEARNING FOR FINANCIAL MARKET MODELLING AND ANALYSIS (MODULE FROM MASTER OF SCIENCE IN FINANCE (FINTECH AND FINANCIAL ANALYTICS))

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Course Information

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Study Mode
Location
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Course Overview

Assessment Methods / 評核方式
(A) Assessment Items and Their Weightings: 1) Class participation / discussion (10%) 2) Assignment(s) (50%) 3) Examination (40%) B) Completion requirements: 1) Pass the course overall assessment with the grade not lower than C- [Passing grade: C- (Grade Point 1.67/4.0 x 100% = approximately 42 marks)] C) CEF reimbursement requirements: 1) Attend 70% of the total contact hours or above 2) Pass the course overall assessment with the grade not lower than C 3) Achieve at least 50% or such higher overall passing mark [Passing grade: C (Grade Point 2.0/4.0 x 100% = approximately 50 marks)]

Entry Requirement / 入學要求
Applicants should possess: a) A Bachelor degree with honours from a recognized university or comparable institution, or a professional qualification deemed to be equivalent; AND b) Proof of English proficiency for applicants whose first degrees were obtained from non-English-medium institutions: a minimum IELTS 6.5 or TOEFL scores 550 (Paper-based) / 79 (Internet-based) or equivalent c) A satisfactory score on the Graduate Management Admission Test (GMAT)/Graduate Record Examination (GRE) is encouraged but not mandatory; d) For Part-Time mode, preference will be given to those with a minimum of 3 years of relevant work experience. e) Mandatory Requirements i) possess basic training/experience in Finance, Mathematics or Computer Science. This requirement can be satisfied by other qualifications. Refer to institution for detailed information. f) Admission interview is required

Instructor's Qualifications / 導師資歷
1. Have a Master's degree or above in related areas 2. Final approval from Provost

QR Number / 資歷名冊登記號碼
19/000803/L6

QF Level / 資歷架構級別
6

CEF Registration Invalid From / 基金課程登記失效日期
01-SEP-27

What You’ll Learn

1) Introduction (6 hours) 2) Supervised Learning (6 hours) 3) Unsupervised Learning (6 hours) 4) Neural Network and Deep Learning (3 hours) 5) Reinforcement Learning and Natural Language Processing (NLP) (3 hours) 6) Machine Learning in Financial Services (9 hours) 7) Operationalizing Machine Learning Models (3 hours) 36 contact hours PLUS 2 to 3 hours examination


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