Preparation
Course Detail
| Key area of study | A04 - COMPUTER SCIENCE AND INFORMATION TECHNOLOGY |
| Course name (English) | MACHINE LEARNING [MODULE FROM MASTER OF SCIENCE IN INFORMATION TECHNOLOGY] |
| Course name (Chinese) | --- |
| Course code | 34Z131257 |
| Institution name (English) | THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY (HKUST) |
| Institution name (Chinese) | 香港科技大學 |
| Institution code | 003 |
| Institution phone |
請點擊查詢院校電話 Please click here for Enquiry Hotline |
| Award | OTHERS |
| Course fee (HK$) | $15500 |
| QR Number | 09/002698/6 |
| QF Level | 6 |
| Remark |
Notes / 附註: *APPLICANT PURSUING THIS COURSE WITH COURSE COMMENCEMENT DATE FALLING AFTER 07 DECEMBER 2024 IS NOT ELIGIBLE TO CLAIM REIMBURSEMENT FROM CEF. / 申請人報讀於二零二四年十二月七日後開課的課程並不能申領基金發還款項。 Entry Requirements / 入學要求: (I) General Admission Requirements of the University: Applicants seeking admission to a master's degree program should have obtained a bachelor’s degree from a recognized institution, or an approved equivalent qualification. (II) English Language Admission Requirements: Applicants have to fulfill English Language requirements with one of the following proficiency attainments: (1) TOEFL-iBT: 80 (2) TOEFL-pBT: 550 (3) TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections) (4) IELTS (Academic Module): Overall score: 6.5 and All sub-score: 5.5 [Applicants are not required to present TOEFL or IELTS score if their first language is English, or they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English.] (III) Program-Specific Admission Requirements: A bachelor's degree in Computer Engineering, Computer Science or a related area Course Outline / 課程大綱: (1) Introduction of Machine Learning, Basics of Probability Theory, Basics of Information Theory (3 hours) (2) Linear Regression, polynomial regression, overfitting and under fitting (3 hours) (3) Logistic Regression (3 hours) (4) Generative Models (3 hours) (5) Machine Learning Theory (3 hours) (6) Feedforward Neural Networks (3 hours) (7) Convolutional Neural Networks (3 hours) (8) Recurrent Neural Networks (3 hours) (9) Variational Autoencoder (3 hours) (10) Generative Adversarial Networks (3 hours) (11) Reinforcement Learning (3 hours) (12) Deep Reinforcement Learning (3 hours) (13) Adversarial Attacks (3 hours) Instructors' Qualifications / 導師資歷: Instructor is the full-time faculty member of HKUST. He/she has obtained PhD degrees in related disciplines and possessed the necessary expertise in the area. Assessment / 課程評核要求: (A) Assessment Items and Their Weightings: (1) Assignment: 12% (2) Programming Assignments: 8% (3) Term Project: 25% (4) Final exam: 55% (B) Completion and CEF Reimbursement Requirements: (1) Grade C, the overall passing grade of the course, equals to 50% of the overall mark of the course. [Grades range from A+ to F. The grades C- to D-, and E, are not used in postgraduate courses. Grade F is a failure grade which cannot be credited toward program graduation requirement.] (2) The applicant must have attended no less than 70% of the contactable hours of the CEF reimbursable course or such higher attendance requirement as prescribed for the CEF reimbursable course (whichever is higher). Delivery Mode / 授課模式: 39 contact hours (face-to-face delivery); FT/PT Course Duration / 課時: 13 weeks Effective Date / 課程生效日期: 08/12/2020 CEF Registration Invalid From / 基金課程登記失效日期: 08/12/2024 |
STEP 3 Reimbursable Courses
List of Reimbursable Courses