University Of Pretoria Computer Science Department

MIT801 - Introduction to Machine and Statistical Learning

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Module Content

Articles - Articles
Clustering
Jain1999.pdf
Data Clustering: A Review
Data Preparation
data_cleaning.pdf
Data Cleaning: Problems and Current Approaches
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
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
K-Nearest Neighbour
572-2580-1-PB.pdf
An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm
UCD-CSI-2007-4.pdf
k-Nearest Neighbour Classifiers
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
Self-Organising Maps
vanHeerden2017_draft.pdf
Self-Organizing Feature Maps for Exploratory Data Analysis and Data Mining: A Practical Perspective
Support Vector Machines
cortes_vapnik95.pdf
Original SVM paper
cs229-notes3.pdf
Notes on SVMs by Stanford Professor Andrew Ng
Assignments - Assignments
Assignment 1
assignment1.pdf
Specification for Assignment 1
IEEEtran.zip
IEEE LaTeX Template
test.csv
Test data
train.csv
Training data
Assignment 2
assignment2.pdf
Specifications
covtype.data.zip
Data
covtype.info.txt
Data description
Assignment 3
Assignment 3 MIT 801.pdf
Assignment 3 MIT 801.pdf
insurance.xlsx
insurance.xlsx
check.xlsx
check.xlsx
Slides - Slides
MIT801_CS_lecture_1.pptx
Lecture 1 Slides
MIT801_L2_handout.pdf
Lecture 2 Slides
Statistical learning Session 1 - Slides and videos
1 Introduction.pdf
Introduction
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
4 Classification.pdf
4 Classification.pdf
3.1 Simple Linear Regression.mp4
3.1 Simple Linear Regression.mp4
3.2 Hypothesis Testing and Confidence Intervals.mp4
3.2 Hypothesis Testing and Confidence Intervals.mp4
3.3 Multiple Linear Regression.mp4
3.3 Multiple Linear Regression.mp4
3.4 Some important questions.mp4
3.4 Some important questions.mp4
3.5 Extensions of the linear model.mp4
3.5 Extensions of the linear model.mp4
3.R Linear Regression in R.mp4
3.R Linear Regression in R.mp4
4.1 Introduction to Classification Problems.mp4
4.1 Introduction to Classification Problems.mp4
4.2 Logistic Regression.mp4
4.2 Logistic Regression.mp4
4.3 Multivariate Logistic Regression.mp4
4.3 Multivariate Logistic Regression.mp4
4.5 Discriminant Analysis.mp4
4.5 Discriminant Analysis.mp4
Statistical learning - Session 1 R related - Statistical learning - Session 1 R related
2 Statistical Learning.R
2 Statistical Learning.R
3 Linear regression.R
3 Linear regression.R
4 Classification.R
4 Classification.R
2.R Introduction to R.mp4
2.R Introduction to R.mp4
3.R Linear Regression in R.mp4
3.R Linear Regression in R.mp4
4.R1 Classification in R.mp4
4.R1 Classification in R.mp4
4.R2 Classification in R.mp4
4.R2 Classification in R.mp4

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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

Lecturers

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:
  • 05 May 12:30-13:30
  • 08 May 08:00-09:00
  • 12 May 12:30-13:30

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