University Of Pretoria Computer Science Department

MIT801 - Introduction to Machine and Statistical Learning


Module Content

Articles - Articles
Data Clustering: A Review
Data Preparation
Data Cleaning: Problems and Current Approaches
Decision Trees
A Survey of Decision Tree Classifier Methodology
A Survey of Decision Tree Classifier Methodology
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
General Topics
Data Mining: the search for knowledge in databases
Supervised Machine Learning: A Review of Classification Techniques
K-Nearest Neighbour
An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm
k-Nearest Neighbour Classifiers
Random Forests
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics
Self-Organising Maps
Self-Organizing Feature Maps for Exploratory Data Analysis and Data Mining: A Practical Perspective
Support Vector Machines
Original SVM paper
Notes on SVMs by Stanford Professor Andrew Ng
Assignments - Assignments
Assignment 1
Specification for Assignment 1
IEEE LaTeX Template
Test data
Training data
Assignment 2
Data description
Slides - Slides
Lecture 1 Slides
Lecture 2 Slides
Statistical learning - Slides and videos
1 Introduction.pdf
2 Statistical Learning.pdf
2 Statistical Learning
3 Linear regression.pdf
3 Linear regression
1.1 Opening Remarks.mp4
1.1 Opening Remarks
1.2 Examples and Framework.mp4
1.2 Examples and Framework
2.1 Introduction to Regression Models.mp4
2.1 Introduction to Regression Models
2.2 Dimensionality and Structured Models.mp4
2.2 Dimensionality and Structured Models
2.3 Model Selection and Bias-Variance Tradeoff.mp4
2.3 Model Selection and Bias-Variance Tradeoff
2.R Introduction to R.mp4
2.R Introduction to R

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Module Links

If you are a machine learning beginner and looking to finally get started using R, this tutorial was designed for you
This small tutorial is meant to introduce you to the basics of machine learning in R: it will show you how to use R to work with the well-known machine learning algorithm called k-nearest neighbors.
Learn to load various formats of data into R
A comprehensive list of the existing packages. Using packages means you do not have to code any algorithms from scratch.
RStudio provides a very intuitive GUI to R. Using RStudio is highly recommended.
An excellent introductory text on NNs and deep learning by Michael Nielsen


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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


Mr Will van Heerden

Mr Sollie Millard

Dr Frans Kanfer

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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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