Course Information
Course title
Introduction to Statistical Learning 
Designated for
Curriculum Number
Curriculum Identity Number
Tuesday 8,9,10(15:30~18:20) 
Restriction: juniors and beyond
The upper limit of the number of students: 30.
The upper limit of the number of non-majors: 10. 
Course introduction video
Table of Core Capabilities and Curriculum Planning
Association has not been established
Course Syllabus
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Course Description

Statistical learning is the process of extracting regularities from data using statistical models with the goal of finding a predictive function based on existing data to be able to make prediction on unseen data of similar type. The course introduces students to the concepts and analytical tools of statistical learning, it emphasizes "learning by doing'' by getting students familiarized with the use of R programming language to perform analysis on empirical data. The first part of the course starts with a refresher on the fundamentals of statistics-mean, variance, distribution, probabilities-before proceeding to more specialized topics. The first part of this course also gives a gentle introduction to R programming, during which issues of dimensionality and balance will be discussed with their diagnostic and preprocessing tasks implemented in R. The second part of the course introduces families of binary, penalized, discriminant, and mixture models, along with performance evaluation metrics. We conclude, in the third part of the course, with emerging data analysis methods such as text mining and network analysis.

Each class meeting usually begins with a lecture on that week's topic. During the lecture, the instructor will instruct students how to perform the analytical tasks by running R on the screen. Lecture note and code will be displayed on class slides and available for download. A total of FOUR course assignments will be given throughout the semester, which will help build the necessary analytical and programming foundation toward the completion of a 10-page term project.

This class is supported by Datacamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises.

You also access all course materials via our shared Dropbox folder: 

Course Objective
After the completion of this course, students will be able to:

1. Distinguish and process different types of data.
2. Identify which classification models to use for a particular dataset and/or modeling assumptions.
3. Perform analytical tasks in R with real-world data.
4. Apply these analytical skills to their (students') own research projects. 
Course Requirement
No prior coding experience in any of the commercial or open source programming languages is required. The course is self-contained in terms of instructing students the basics of programming necessary to perform the analytical tasks covered in this course. We will be using R, a versatile open-source programming language, as the primary programming language for instruction. RStudio will be the main instructional IDE (Integrated Development Environment) for running R applications in this course, but your are free to use other IDEs of your choice. Students are encouraged to constantly practice running R as well as explore alternative ways of doing the same tasks to get the most out of the practical aspect of this course. If you prefer to use other programming languages instead (e.g., Python, Matlab, Stata), I am open to discuss how I can better accommodate your needs. 
Student Workload (expected study time outside of class per week)
8-10 hours per week plus self-administered programming exercise 
Office Hours
Note: Please make an appointment with me. 
Please refer to the syllabus. 
Designated reading
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning, with Applications in R (New York, NY: Springer Nature, 2013).

Max Kuhn and Kjell Johnson, Applied Predictive Modeling (New York, NY: Springer Nature, 2013).

J. Scott Long. 1997. Regression Models for Categorical and Limited Dependent Variables (Thousand Oaks, CA: SAGE Publications).

Other readings are sourced from book chapters and articles published in academic journals and websites. Specific readings for each class are identified on this syllabus. Readings marked with a * will be available on course website; readings marked with a "v" means "review'' from past weeks. Items marked with a "globe" are clickable web-based materials. Items marked with a blacksquare are brief introduction on specific subjects provided by the instructor. 
Explanations for the conditions
Weekly readings and exercises 
Making efforts to keep track course progress is essential. You are expected to have finished assigned readings before each week's meeting and practice assigned R analytical exercises to increase your proficiency with key statistical learning concepts and R programming. 
A total of FOUR data analysis assignments will be given every 3-4 week to give students hand-on opportunities to apply their analytical and programming skills to real data from selected topic areas. Students are allowed to form study group to discuss assignments, reference textbooks, or make use of crowdsourcing Q&A forums, such as stackoverflow, quora and reddit. Remember, the instructor and TA are always at your service. Submitted assignments need to be your own works. You are encouraged to discuss assignments with your peers but you are FORBIDDEN to submit duplicated answers or have someone do the assignments for you. 
Term research paper 
At the end of the semester, students are required to submit an analytical paper of approximately 10-12 pages (but no more than 15 pages), centering on drawing statistical inference from the analysis of a dataset (or multiple datasets). There will be no assigned topics; instead, students will use their own discretion to select research topics from the social science or other cognitive fields, so long as you are using the analytical concepts and tools acquired in this course to approach them. Students will need to submit their topics at the 10th class meeting (insert date) and are encouraged to schedule an appointment with the instructor to discuss their topics. 
Adjustment methods for students
Teaching methods
Provide students with flexible ways of attending courses
Assignment submission methods
Extension of the deadline for submitting assignments, Written report replaces oral report, Individual presentation replace group presentation
Exam methods
Written (oral) reports replace exams
Negotiated by both teachers and students
Week 1
2/21  Course introduction 
Week 2
2/28  228 Peace Memorial Day (No class) 
Week 3
3/7  Regression Methods I 
Week 4
3/14  Regression Methods II 
Week 5
3/21  Data Dimensionality and Preprocessing 
Week 6
3/28  Nonlinear Regression I 
Week 7
4/4  Women and Children's Day (No class) 
Week 8
4/11  Nonlinear Regression II 
Week 9
4/18  Statistical Learning I 
Week 10
4/25  Statistical Learning II 
Week 11
5/2  Statistical Learning III 
Week 12
5/9  Dimension Reduction and Prediction Accuracy 
Week 13
5/16  Resampling Methods 
Week 14
5/23  Drawing Inference from Text Data I 
Week 15
5/30  Drawing Inference from Text Data II 
Week 16
6/6  TBD (or term paper advising) 
Week 17
6/13  Final (no class) 
Week 18
6/20  Final (no class)