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Introduction to Computer Science and Programming
Basics of Machine Learning and Data Analysis
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选课操作指南
The Outline of《Basics of Machine Learning and Data Analysis》
Basic Teaching Information
Course Code：2000520013003 |
Course Title：Basics of Machine Learning and Data Analysis |
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Faculty：Computer Science |
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Course Credit： |
Lecture Hours：16 （consisted of __16___ theoretical hours ） |
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Course Type：General Course(通识课程) |
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Related Preview Courses： Linear Algebra, Probability Theory and Mathematical Statistics, Fundamentals of Machine Learning |
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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.
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 |
1.5 hours |
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 |
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
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/
