MIT801  Introduction to Machine and Statistical Learning
Announcements
Access to Campus
Posted By: Prof Andries Engelbrecht editied on: Mon 30 Jan 2017, 08:18:59
Global Announcement
Note that access to campus is only possible once you have registered and you have an activated student card.
2016 Exam Perusals
Posted By: Prof Andries Engelbrecht editied on: Mon 30 Jan 2017, 08:18:17
Global Announcement
Exam perusals for the Nov/Dec 2016 exams will take place 7 Feb 09:30 to 12:30 and 8 Feb 09:30 to 12:30, in IT 466.
Exam
Posted By: Ms Anna Bosman editied on: Wed 21 Jun 2017, 08:04:55
The Exam paper is available under the "Examination". Please submit a single document that contains all of your answers. The front page should be signed, scanned (or photographed) and added to the document that contains your answers. The preferred submission file format is PDF. You may use any text editor to type your answers. Good luck!
Marks released
Posted By: Ms Anna Bosman editied on: Tue 20 Jun 2017, 15:59:30
Dear students, The preliminary semester marks have been released. Please find it under the "Marks" folder. Please note that Assignmnent 1 mark is outstanding, and will be added shortly. Good luck with the exam tomorrow! The paper will become available on the course page at 7:30 in the morning tomorrow.
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 MultiDisciplinary 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 
KNearest Neighbour
57225801PB.pdf
An Empirical Study of Distance Metrics for kNearest Neighbor Algorithm 
UCDCSI20074.pdf
kNearest 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 
SelfOrganising Maps
vanHeerden2017_draft.pdf
SelfOrganizing Feature Maps for Exploratory Data Analysis and Data Mining: A Practical Perspective 
Support Vector Machines
cortes_vapnik95.pdf
Original SVM paper 
cs229notes3.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
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 BiasVariance Tradeoff.mp4
2.3 Model Selection and BiasVariance 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 
Statistical learning Session 2
 Statistical learning Session 2
5 Resampling.pdf
5 Resampling.pdf 
10 Unsupervised.pdf
session 2 june.zip 
hastie_par5_2.pdf
session 2 june.zip 
10.3 kMeans Clustering.mp4
session 2 june1.zip 
5.4 The Bootstrap.mp4
session 2 june1.zip 
5.5 More on the Bootstrap.mp4
session 2 june1.zip 
Statistical learning Session 2  R related
 Statistical learning Session 2  R related
10.R2 Unsupervised in R.mp4
session 2 june2.zip 
5.R2 Resampling in R.mp4
session 2 june2.zip 
10.R.R
session 2 june2.zip 
5 ResamplingS.R
session 2 june2.zip 
5.R.R
session 2 june2.zip 
5.R.RData
session 2 june2.zip 
Marks
 Marks
MIT 801 Assignment marks
MIT 801 Assignment marks 
Module forums
The new CS forums are available here.
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 wellknown machine learning algorithm called knearest 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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 05 May 12:3013:30
 08 May 08:0009:00
 12 May 12:3013:30
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