Introduction to Statistical Learning and Machine Learning (Fall 2018)

1. James, Witten, Hastie and Tibshirani (2013) An Introduction to Statistical Learning, with applications in R. Springer.

2. Bishop, C.M. (2006), Pattern recognition and Machine Learning, Springer.

3. Hastie, T., Tibshurani, R. and Friedman, J. (2011) The Elements of Statistical Learning, data mining, inference and Prediction, 2ndEdition. Springer.

(1) Class attendance (10%), includes class performance, class discussion and critical thinking.

Each absent: -1%

(2) Weekly homework (20%), is of 5 times. We expect the student can finish each one within 1.5-2.5 hours.

Each: 4% *5;

Late Submission (after Dec. 14th, 2017) will be penalized by 50% of total score of each homework, in other word, the highest score for each one of the late submission is 2%.

(3) Monthly mini-projects (50%), is of 3-4 projects which are selected from the real-world Big-Data problems, including but not limited to, computer vision, pattern recognition, recommendation system, social network, financial data analysis and bioinformatics. In general, the reports should be written in English, and include algorithm skims (3%), critical codes (2%), experimental analysis (3%); and the discussion of proposed method (2%).

About the Submission of mini-projects.

The report can be written by Word or Latex.  Generate a single pdf file of your mini-projects. The file name should be SLML_yourname_student-id.pdf.  Also put the names and Student ID in your paper. To submit the report, email the pdf file to 15955038579@163.com.  About the deadline and penalty. In general, you should submit the paper according to the deadline of each mini-project. The late submission is also acceptable; however, you will be penalized 10% of scores for each week's delay.

(4) Final project (20%) is finished by one team. Each team should have up to 3 students; and will solve a real-world Big-Data problem. In general, the final report should be written in English. The main components of the report will cover (1) introduction to background and potential applications (2%); (2) Review of the state-of-the-art (3%); (3) Algorithms and critical codes in a nutshell (10%); (4) Experimental analysis and discussion of proposed methodology (5%).

Reference books:

Math cookbook   Linear Algebra Review

Note that:

(1) mini-projects are not allowed to use any existing toolbox; you have to write every line codes by yourself.

(2) In final project, you can use the toolbox.　

(3) Meanwhile, we will randomly check some students' projects by asking his/her some questions, in order to validate that the projects are done by himself/herself.