Introduction to Data Mining with R

STAT2450, Winter 2016
Dalhousie University

Dr. Hong Gu Professor and Course Supervisor hgu@mathstat.dal.ca

Mathew Kallada Student Instructor kallada@cs.dal.ca

News
Syllabus
Textbook
Calendar
Practice
Assignments
The tenative course calendar is shown below along with the topics discussed on that date. Lecture slides for each class will appear after corresponding date.


Introduction to Data Mining
January 5, 2016 Lecture Notes Lecture Slides

Supervised Learning with K-Nearest Neighbors
January 7, 2016 Lecture Notes Lecture Slides

R Lesson #1: Arithmetic Operations, Variables, If statements
January 12, 2016 Lecture Notes Lecture Slides

R Lesson #2: Loops; Training a KNN classifier using R
January 14, 2016 Lecture Slides

Training KNN in R, Changing the Complexity, and Hold-out Testing
January 19, 2016 Lecture Slides

Training & Visualizing a Decision Tree classifier with R
January 21, 2016 Lecture Slides

The Bias-Variance Trade-off: Finding the Optimal Model Complexity and K-Fold Cross-Validation
January 26, 2016 Lecture Slides

GridSearch Continued: Finding the optimal model complexity
January 28, 2016 Lecture Slides

R Lesson #4: Data Visualization Tasks with R
February 2, 2016 Lecture Slides

Solving Supervised Learning Tasks with Support Vector Machines
February 4, 2016 Lecture Slides

Class Cancelled
February 9, 2016

Class Cancelled
February 11, 2016

Solving Supervised Learning Tasks with Artificial Neural Networks: Perceptron
February 16, 2016 Lecture Slides

Solving Supervised Learning Tasks with Artificial Neural Networks: Multi-layer Perceptron
February 18, 2016 Lecture Slides

Confusion Matrices and Learning Curves
February 23, 2016 Lecture Slides

Introduction to Ensemble Learning and Bagging
February 25, 2016 Lecture Slides

Random Forests (cont'd)
March 1, 2016

Introduction to Unsupervised Learning and the DBSCAN Clustering Method
March 3, 2016 Lecture Slides

Introduction to Unsupervised Learning and the DBSCAN (Cont'd)
March 8, 2016

Inroduction to Unsupersvied Learning: K-Means Clustering, PCA, and Autoencoders
March 10, 2016 Lecture Slides

Autoencoders and Random Forests in R
March 15, 2016 Lecture Slides

Neural Networks in R, Precision-Recall Graphs
March 17, 2016 Lecture Slides

Review of PCA in R
March 22, 2016 Lecture Slides

Feature Selection Approaches
March 24, 2016 Lecture Slides

Final Exam Review
March 29, 2016 Lecture Slides