CSCI5150 Machine Learning Algorithms and Applications

 

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 business intelligence, natural language processing, computer vision, and sensor-based data analyses, including four topics that consist of (1) supervised learning algorithms induced by structural risk minimization for classification and regression problems (decision trees, logistic regression, support vector machines, regularized linear regression, kernel machines, etc.), and their applications in sensor-based indoor localization, business intelligence; (2) supervised learning algorithms based on deep learning (CNN, RNN, etc.), and their applications to natural language processing and computer vision; (3) unsupervised learning algorithms for clustering and representation learning (K-means, spectral clustering, autoencoder, etc.); (4) introductions of other learning algorithms and applications, such as transfer learning, recommender systems, sensor-based activity recognition, etc.
本科以四個主題介紹機器學習在商業智能、自然語言處理、計算機視覺和基於傳感器的數據分析中的典型應用,四個主題包括(1)分類和結構風險最小化誘導的監督學習算法和 回歸問題(決策樹、邏輯回歸、支持向量機、正則化線性回歸、核機等)及其在基於傳感器的室內定位、商業智能中的應用;(2)基於深度學習(CNN、RNN等)的監督學習算法,及其在自然語言處理和計算機視覺中的應用;(3)用於聚類和表示學習的非監督學習算法(K-means、譜聚類、自動編碼器等);(4)其他學習算法和應用的介紹,如遷移學習、推薦系統、基於傳感器的活動識別等。
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 be able to:
1. Apply appropriate machine learning algorithms to solve specific real-world applications.
2. Revise or design new machine learning algorithms based on specific requirements.
Assessment
(for reference only)
Project: 50%
Homework and Assignment:40%
Presentation:10%
Recommended Reading List 1. Introduction to Machine Learning (2nd Ed.), by EthemAlpaydin, The MIT Press, 2010.
2. Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2005.
3. Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, The MIT Press, 2016. https://www.deeplearningbook.org/
4. Learning with Kernel, by Bernhard Scholkopfand and Alex Smola, The MIT Press, 2002.

 

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