University Of Pretoria Computer Science Department

MIT801 - Introduction to Machine and Statistical Learning

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Module Content

Articles
Clustering
Jain1999.pdf
Data Clustering: A Review
General Topics
Holsheimer1994.pdf
Data Mining: the search for knowledge in databases
Supervised_Machine_Learning_A_Review_of.pdf
Supervised Machine Learning: A Review of Classification Techniques
Self-Organising Maps
vanHeerden2017_draft.pdf
Self-Organizing Feature Maps for Exploratory Data Analysis and Data Mining: A Practical Perspective
Decision Trees
A Survey of Decision Tree Classifier Methodology
A Survey of Decision Tree Classifier Methodology
murthy1998.pdf
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Random Forests
bbs034.pdf
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
TR.pdf
Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics
K-Nearest Neighbour
UCD-CSI-2007-4.pdf
k-Nearest Neighbour Classifiers
572-2580-1-PB.pdf
An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm
Data Preparation
data_cleaning.pdf
Data Cleaning: Problems and Current Approaches
Assignments
Assignment 1
assignment1.pdf
Specification for Assignment 1
IEEEtran.zip
IEEE LaTeX Template
train.csv
Training data
test.csv
Test data
Slides
MIT801_CS_lecture_1.pptx
Lecture 1 Slides

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Module Description

In this module students will be exposed to different categories of machine and statistical learning algorithms that can be used to manipulate big data, identify trends from the da...

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Lecturer Information

Course Coordinator

Ms Anna Bosman

Lecturers

Mr Will van Heerden

Mr Sollie Millard

Dr Frans Kanfer

Assistant Lecturers

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H.o.D Office times

During the times below, any student can come and discuss any issue with the head of the department without making an appointment. Note that these dates and times may change, and any such changes will be updated on each module home page.

The dates and times are as follows:
  • 3 February 12:30-13:30
  • 10 February 12:30-13:30
  • 13 February 08:00-09:00
  • 17 February 12:30-13:30
  • 20 February 08:00-09:00
  • 27 February 08:00-09:00
  • 6 March 08:00-09:00
  • 10 March 12:30-13:30
  • 13 March 08:00-09:00
  • 17 March 12:30-13:30
  • 24 March 12:30-13:30
  • 27 March 08:00-09:00
  • 31 March 12:30-13:30

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