CS5489 Machine Learning: Algorithms and Applications (2023 Fall Course Materials)

YongzeYang (Yongze Yang) · E-commerce · Intake 2023 · Learning assistance

Repository: YongzeYang/CityU-CS5489-Machine-Learning-Algorithms-and-Applications-23fall

A collection of Learning materials for the postgraduate course CS5489 Machine Learning: Algorithms and Applications of the Department of Computer Science, City University of Hong Kong. The author took this course in the autumn semester of 2023. The warehouse contains the tutorial classes, assignments, codes and reports of group projects completed by himself (and his group members), as well as the mid-term and final exam papers from previous years, which are convenient for review. These are all the author's own assignments and not standard answers.

• cs5489
• machine-learning
• course
• kaggle
• jupyter
• python
• past-papers
• 23fall
• go
• machine learning
• llm

Project overview

A collection of learning materials for CS5489 Machine Learning: Algorithms and Applications, a postgraduate course in computer science at the City University of Hong Kong.The author took this course in the autumn semester of 2023. The warehouse contains the codes and reports of the tutorials, assignments, and group projects completed by the author (and the team members). In addition, the mid-term and final exam papers of previous years were collated to facilitate review.These are the authors' own assignments, not standard answers.This course focuses on the practical application of machine learning, and all exercises, assignments and projects are completed with Jupyter Notebook, which is no small workload. The students who are preparing to take this class or are taking this class can first look at its content and rhythm. Function 10 coaching sessions : Practice notebook for each lesson, full marks except for deducting 1 point for each of the two times 2 Assignments : Assignment 1 does the text classification, Assignment 2 does the audio classification, and the results are submitted to the course Kaggle; each time, a final protocol notebook, a complete experimental report, and a csv for submission are attached. Group Items Selected is the Kaggle competition "LLM - Detect AI Generated Text", along with the project report, submitted code and presentation slides Previous year's paper : 5 sets of mid-term questions (with answers) and 2 sets of final questions (questions only) Curriculum Information The assessment structure for the autumn semester of 2023 is as follows: 10% for tutorial classes, 10% for each of the two assignments, 30% for group projects, 10% for mid-term exams, and 30% for the final exams. Both group projects and the final exams must achieve a score of over 30% to pass Bilingual README in Chinese and English The warehouse does not contain reference answers, courseware or course recordings for tutorial classes. The content is for reference only