CS 489 -- Resources (Spring 2017)

Lecture slides:

• Day 1: Introduction

• Day 2: Introduction of Machine learning

• Day 3: Machine learning basic

• Day 4: Bayesian decision theory

• Day 5: Naive Bayesian algorithm

• Day 6: Model based machine learning applications

• Day 7: Bayesian network and EM algorithm

• Day 8: Bayesian network, EM algorithm, decision tree

• Day 9: Decision tree I

• Day 10: Decision tree II

• Day 11: Logistic Regression

• Day 12: Linear and Logistic Regression

• Day 13: Introduction to Neural Network I

• Day 14: Introduction to Neural Network II

• Day 17: Introduction to Neural Network III

• Day 18: Backpropagation algorithm

• Day 19: ML advices and final project in-class discussion

• Day 20: Deep learning

• Day 21: Recurrent neural network

• Day 22: Invited talk for PCA

• Day 25: Introduce of ML libraries and SVM

• Day 26: SVM 2

• Day 27: SVM 3

• Day 28: SVM 4

• Day 29: Unsupervised learning

• Day 33: The last class

Class Materials:

• Account Requests via the CS HUB

Other useful software:

• Rstudio, great R IDE
• Atom editor
• FileZilla, PuTTY, OpenSSH and WinSCP Clients via the HUB (tools and instructions for accessing your files on the CS server).
• Eclipse (free, full featured Python IDE)