The Outline of《Basics of Machine Learning and Data Analysis》

1. Basic Teaching Information

Course Code：2000520013003		Course Title：Basics of Machine Learning and Data Analysis
Faculty：Computer Science		Targeted Student：
Course Credit：		Lecture Hours：16 （consisted of __16___ theoretical hours）
Course Leader：	Name：		E-mail：
	Office：		Mobile：
Course Staff：	Name：		E-mail：
	Office：		Mobile：
Course Type：General Course(通识课程)
Related Preview Courses： Linear Algebra, Probability Theory and Mathematical Statistics, Fundamentals of Machine Learning

1. Course Introduction

   We present an introduction to basic machine learning algorithms. We will first introduce unsupervised and supervised machine learning algorithms through a set of basic algorithms in each class. We will then present their applications to Recommendation Systems, for movies, music and books,

   used by companies like Netflix, Spotify, and Amazon, in their big data analytics software systems. Such systems are major drivers of business today.

2. The Allocation of Content and Lecture Hours

Content	Lecture Hours
Lecture 1: What is machine learning? What is Artificial Intelligence? Algorithms: types of machine learning Data science: similarity, proximity and classification	1.5 hours
Lecture 2: Unsupervised Learning: K-mean Clustering Algorithm	1.5 hours
Lecture 3: Unsupervised Learning: Spectral Clustering Algorithms	1.5 hours
Lecture 4: Supervised Learning: Linear Regression, Logistic Regression	1.5 hours
Lecture 5: Supervised Learning: Decision Trees, Random Forests	1.5hours
Lecture 6: Supervised Learning: Artificial Neural Networks, Perceptual Machines and Deep Learning	1.5 hours
Lecture 7: Applications: Recommender Systems I	1.5 hours
Lecture 8: Applications: Recommender Systems II	1.5 hours

1. Assessment Methods and Marking Criterion

   the total grade 100% = attendance 10% +homeworke 30% +final exam 60%

   Grades are based on a five-point system of A, B, C, D, and F, with the following correspondence：

   A：90-100

   B：80-89

   C：70-79

   D：60-69

   F：59 and below

2. Textbooks and References

Book 1: J. Leskovec, A. Rajaraman, J. Ullman (second edition), “Mining Massive Datasets” Cambridge University Press 2019

PPTs slides:

http://www.mmds.org/#ver10

Book 2: Tom Mitchel “Machine Learning”, 1997

PPTs slides:

http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

http://www.cs.cmu.edu/~tom/mlbook.html

Book 3: P-N Tan, M. Steinbach, V. Kumar, “Introduction to Data Mining” (second edition), Pearson Publ., 2023

PPTs slides:

https://www-users.cse.umn.edu/~kumar001/dmbook/index.php

Book 4: C. Manning, P. Raghavan, H. Schutze, “Introduction to Information Retrieval”, Cambridge University Press, 2008

PPTs slides:

https://www.cs.odu.edu/~sampath/courses/w17/cs599/