UIUC_1_1_Applied Machine Learning

You are allowed to read all the codes and files, but you are not allowed to copy directly for your assignments!!!!!!! I will not take any responsibility if you break the honor rule.
Please try yourself and have fun working on those interesting projects!

Courese Information：

1. Course: CS 498 Applied Machine Learning
2. Time: 2018/08/271- 2018/12/15
3. Teacher: Trevor Walker ( I will definitely recommand this course. Trevor is an amazing professor but since I have too much things this semester, I did not go to course often in the last half of semester. I will watch those videos later. Trevor is amaing and just soooooo good to teach Machines Learning. The only thing I don't like is that there are 11 homeworks, almost one for each week. Sometimes it's to much with just one week ddl during midterm week or other time. And TAs are just ( Don't know what they did... Even we don't hand in source code and no one really care about this. Good professor, good course expect TAs...Honestly, I don't know what they did. Even not grading the assignmnet...)

Program Infromation:

1. Linux + Ubuntu 16.04
2. See each program/homework under different folders. I will not upload any resources from professor, I will only upload my work including homework, source code and related notes.

HW1

• Naive Bayes Classifier
• SVM
• Decision Forest

HW2

• support vector machine

HW3

• PCA

HW4

• PCA +PCoA

HW5

• k-means

HW6

• linear regression
• outlier
• Box-Cox transformation

HW7

• Linear Model: Poisson Model + Lasso
• Cross-Validated
• Regression

HW8

• Image Segmentaion using EM

HW9

• Mean field inference
• oltzmann machine model

Tips:

1. All codes and notes will be open source after my final test (in case of copy)
2. You are allowed to read all the codes and files, but you are not allowed to copy directly for your assignments.
3. If you have any problems or ideas want to share with me, please feel free to e-mail to me: layla.laisy@gmail.com