| Course code | CSCI5150 |
| Course title | Machine Learning Algorithms and Applications 機器學習算法與應用 |
| Course description | This course introduces a dozen of machine learning algorithms and typical applications in computational finance, bioinformatics, and other big data analyses, including six topics that consist of (1) unsupervised learning algorithms for clustering analysis, local subspaces, manifold learning, and their applications in image analysis and bioinformatics; (2) arbitrage pricing theory (APT) and temporal factor analysis for finance market modelling and stream data analysis；(3) supervised learning algorithms(decision tree and deep learning) for pattern recognition, (4) learning biomedical case-control analyses and machine fault detection from an integrative view of description, classification, and hypothesis test；(5) algorithms of graph analysis and nonnegative matrix decomposition for learning biology networks and social computing; (6) brief introductions of other learning algorithms such as transfer learning, recommendation systems, etc . 本科以六個題目講授機器學習在計算金融、生物信息學、大數據分析有典型應用的十餘種學習算法。一是針對圖像分析和生物信息學應用，介紹聚類分析、局部子空間 、流形學習等非監督學習算法；二是針對計算金融和數據流分析，介紹APT市場模型和時序因子分析算法；三是用於模式識別的監督學習算法（決策樹和深度學習）；四是針對生物數據分析和機器故障診斷，介紹支持向量機學習、稀疏學習，以及綜合假設檢驗－判別－預測新方法；五是用於網絡生物學和社會計算的圖分析算法和非負矩陣分解算法。六是簡介轉移學習、推薦系統等其他學習算法。 |
| Unit(s) | 3 |
| Course level | Postgraduate |
| Exclusion | FTEC5580 |
| Semester | 1 or 2 |
| Grading basis | Graded |
| Grade Descriptors | A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. |
| Learning outcomes | At the end of the course of studies, students will have acquired the ability to 1. understand major algorithms and typical applications of unsupervised learning. 2. understand typical algorithms and applications of learning temporal models. 3. understand typical algorithms and applications of supervised learning. 4. understand typical algorithms and applications of case-control analyses and fault detection. 5. understand typical algorithms and applications of learning networks structures. |
| Assessment (for reference only) |
Short answer test or exam：55% Selected response test or exam：15% Others：30% |
| Recommended Reading List | 1. Marsland, Stephen. Machine learning: an algorithmic perspective. CRC Press, 2011. 2. Kantardzic, Mehmed. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, 2011. 3. James T. Kwok, Zhi-Hua Zhou and Lei Xu, Machine Learning, a chapter in Springer Handbook of Computational Intelligence, 2013. |
| CSCIN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. identify, formulate, and solve computer science problems (K/S); | T |
| 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); |
T |
| 3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); | TP |
| 4. communicate effectively (S/V); |
|
| 5. succeed in research or industry related to computer science (K/S/V); |
T |
| 6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); | TP |
| 7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V); | |
| 8. practise high standard of professional ethics (V); | |
| 9. draw on and integrate knowledge from many related areas (K/S/V); |
T |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured | |