CSCI-B 365 Data Analysis and Mining
This course serves as an introduction to Data Analysis and Data Mining, in which we extract knowledge and understanding from data in an algorithmic and visual ways. We will learn about probability as a language that supports and unifies our understanding of data. We will build models using probability and turn these models into algorithms for data analysis. The aim is for students to master some basic probabilistic grounding, learn several popular algorithms, and develop experience thinking critically about the overall process of understanding and interpreting data.
Course information |
Instructor: Xiaojing Liao (xliao@indiana.edu) |
Time: Monday, Wednesday 4:00 pm - 5:15 pm |
Place: Info East 150 |
Office hours: Tuesday, Thursday 4:00 pm - 5:00 pm |
Textbook |
<Principles of Data Mining> by Max Bramer e-copy available in IU Library The following books are recommended to read: [1] <Introduction to Data Mining> by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar [2] <Data Mining: Concepts and Techniques> by Jiawei Han and Micheline Kamber |
Week |
Date |
Agenda |
Reading |
HW |
Week 1 |
1/7 |
Course overview Data representation |
Syllabus/ Textbook chapter 2 |
|
1/9 |
Data representation/ Probability |
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Week 2 |
1/14 |
Probability |
Conditional probability chapter 2 |
|
1/16 |
Probability |
HW1 |
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Week 3 |
1/21 |
No class (MLK Jr. Day) |
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1/23 |
Probability |
Conditional probability chapter 3 & 6 |
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Week 4 |
1/28 |
Probability |
Conditional probability chapter 7 |
HW2 |
1/30 |
No class (severe winter weather) |
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Week 5 |
2/4 |
Probability |
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2/6 |
Classification |
Textbook chapter 3.1 |
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Week 6 |
2/11 |
Classification |
Textbook chapter 3.2 |
HW3 |
2/13 |
Classification |
Textbook chapter 3.2 |
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Week 7 |
2/18 |
Classification |
Textbook chapter 3.3 |
|
2/20 |
Classification |
Textbook chapter 3.3/7.2 |
HW4 |
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Week 8 |
2/25 |
No class (travel for conference) |
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2/27 |
No class (travel for conference) |
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Week 9 |
3/4 |
Classification |
Textbook chapter 4.1/5.3 Intro to Tree Classifier |
|
3/6 |
Classification |
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Week 10 |
3/11 |
Spring Break |
||
3/13 |
Spring Break |
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Week 11 |
3/18 |
Classification |
Textbook chapter 9.2/9.4 |
|
3/20 |
Regression |
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Week 12 |
3/25 |
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3/27 |
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Week 13 |
4/1 |
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4/3 |
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Week 14 |
4/8 |
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4/10 |
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Week 15 |
4/15 |
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4/17 |
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Week 16 |
4/22 |
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4/24 |
Final exam review and wrap up |
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4/29 |
Final Exam (2:45pm - 4:45pm) |
Prerequisites |
Students should have basic programming skills such as would be acquired through CSCI-C 200, C-211 or INFO-I 210. |
Grading |
40% of Homework; 25% of In-class Midterm exams; 35% of Final Exam |
Others |
Laptop policy: Using laptop/tablets is only allowed to take notes |
Academic accommondation: It is the policy of Indiana University Bloomington to accommodate students with disabilities and qualifying diagnosed conditions in accordance with federal and state laws. Any student who feels s/he may need an accommodation based on the impact of a learning, psychiatric, physical, or chronic health diagnosis should contact Office of Disability Services for Students (DSS) to determine if accommodations are warranted and to obtain an official letter of accommodation. For more information, please click here. |
Honor code: Students are required to follow the Honor System of Indiana University Bloomington |