Introductory Applied Machine Learning
Course Homepage
Lecturers:
Nigel Goddard
TAs: TBC
Stefanos Angelidis,
Victor Jose Hernandez Urbina
Course reps:
Victor Chen s1572402@sms.ed.ac.uk, Sanchit Gupta s1348153@sms.ed.ac.uk
The goal of this course is to introduce students to basic algorithms
for learning from examples, focusing on classification and clustering
problems.
This is a level 10 course intended for MSc students and 3rd/4th year
undergraduates.
For an overview of the planned course topics, see the
Course Catalog entry and
the 2015 introductory handout.
News
16/11/2015  The kNN quiz and Mixture Models/EM quiz are online, please complete by Wednesday for review on Thursday.
9/11/2015  The Clustering quiz and remaining online lectures are posted.
1/10/2015  Naive Bayes quiz posted. ITO should be allocating tutorials and labs by the end of the week.
28/9/2015  I will use the results of the quizzes this week (week 2) to decide what, if anything, to do in the lecture slots next week (week 3). If everyone is doing well in the quizzes, we will skip the lecture slots, otherwise there will be an optional review session for those that need it. Check the schedule below to see if a review is happening.
28/9/2015  For the next few weeks this webpage will be updating quite frequently, so check it. I will use the email list for important announcements but not every change.
27/9/2015  Lectures this week and next will be online. Review the videos, then take the quiz. If you are not satisfied with your quiz performance, review the videos for the relevant slides (see the revise links).
Lectures
Lecture slots are
14:1015:00 Mondays and Thursdays in
Appleton
Tower, Lecture Theatre 3
This year for the first time we will be trialling some
"flippedclassroom" methods, and will be looking for student feedback
during the course how this is working for you. Approximately half of
the material will be delivered in the traditional fashion via a
lecture during the lecture slots above. The other material will be
delivered via a combination of online short video segments (overall
approximately the same length as a traditional lecture), which you
should watch before the lecture slot. During some of the lecture
slots, we will have other activities to review the material in the
videos.
Resources
Discussion Forum
You will get an email invite to join the forum.
The forum will contain detailed lecture notes annotated with questions
and answers. It is monitored by the lecturer and the TAs, so
if you ask questions here, you are likely to get a much faster
response than if you email the lecturer or TAs individually. (However, if
you have issues that should be kept confidential, then of course
please do email the course lecturer.)
Assignments
There will be two equallyweighted assignments together worth 25% of
the mark for the course. These will be released, and submissions due,
according to the schedule below.
Assignment 
Released 
Due 
1 
Week 5, Monday, 4 p.m. 
Week 7, Monday, 4 p.m. 
2 
Week 8, Friday, 4 p.m. 
Week 11, Monday, 4 p.m. 
You are required to submit both an electronic copy and a manual copy
to the ITO by the deadlines. The deadlines are strictly enforced.
Assignment #1
The question sheet is here: assignment1.pdf
[discuss].
It is due at 1600 on Monday in Week 7 (Monday 2nd November
2015)
by manual submission
to ITO and electronic submission (please see question sheet for detailed
submission instructions). The data files are here:
train_20news_partA.arff,
train_20news_partB.arff
train_auto_partA.arff,
train_auto_partB_numeric.arff.
Marks will be returned within two weeks of
the due date.
Assignment #2
The question sheet is here: assignment_2.pdf
[discuss].
It is due at 1600 on Monday in Week 11 (Mon 30 Nov 2015)
by manual submission
to ITO and electronic submission (please see question sheet for detailed
submission instructions). The data files are here:
cluster_means.txt,
train_20news_partA.arff,
train_mnist_dd01_partB.arff,
train_mnist_dd02_partB.arff,
train_mnist_binary_partC.arff,
train_mnist_binary_pairConj_partC.arff,
train_images_partD.arff,
valid_images_partD.arff,
test_images_partD.arff.
The website where you can view images like the ones in the training and validation sets is here here.
Marks will be returned within two weeks of
the due date.
Please read the Informatics policy on late submissions and plagiarism.
Tutorials
Tutorials will be in weeks 3, 5, 7 and 9.
[
Groups, times and rooms]
Labs
Labs will be in weeks 3,5,7 and 9.
[
Groups, times and rooms]
WeekbyWeek listing
(This list is subject to change.)
Week 1
Lecture Theatre:
Tutorials: No tutorials in week 1 (nor week 2)
Readings: Textbook chapters 1, 2
Mathematical preliminaries: These
Supplementary Mathematics notes
are from the old Learning from Data course. They are
more difficult than what we will need for IAML, but if
you are happy with them you should have no problem with
the IAML maths level.
Week 2
Lecture Theatre: None
Online Lectures:
Tutorials: No tutorials in week 2
Readings: Textbook chapters 7.1, 7.2
Week 3
Lecture Theatre:
Thu: 
Review: Thinking about Data and Naive Bayes classification

Online Lectures:
Tutorial 1: Naive Bayes and Data Representation
[
solutions]
Lab 1: Naive Bayes classification
Readings: Textbook chapters 4.2
Week 4
Lecture Theatre:
Mon: 
Free

Thu: 
Review: Generalisation and Evaluation

Online lecture
Tutorials: No labs or tutorials in week 4
Readings: Textbook chapters 3.2, 3.3, 4.3, 6.1, 6.5
Week 5
Lecture Theatre:
Mon: 
Lecture: Linear regression
[discuss]
[notes]
[video(2014)] 
Thu: 
Review: Decision Trees

Tutorial 2: Decision Tree and Gaussian Naive Bayes
[
solutions]
Lab 2: Attribute selection and Regression
Readings: Textbook chapter 4.6 (but pairwise classification, perceptron learning, Winnow are not required)
Week 6
Lecture Theatre:
Readings: Textbook chapter 4.6 (but pairwise classification, perceptron learning, Winnow are not required); 6.3 (max margin hyperplane, nonlinear class boundaries),
SVM handout. SV regression is not examinable.
Week 7
Lecture Theatre:
Mon: 
Lecture: Support vector machines Part II
[slides]
[pdf]
[video (2014)]

Thu: 
Review: Nearest Neighbour

Online lecture
Readings: Textbook chapters 5, 4.7, 6.4
Tutorial 3: Logistic regression
[
solutions]
Lab 3: Support Vector Machines, Evaluation
Week 8
Lecture Theatre:
Mon: 
Free

Thu: 
Review: kMeans clustering

Online Lectures/Quizzes
Readings: Textbook chapters 4.8, 6.6
Week 9
Lecture Theatre:
Mon: 
Free

Thu: 
Review: Mixture Models, EM and Nearest Neighbours

Online lectures
Tutorials: SVMs, Clustering
[
solutions]
Lab 4: PCA, Clustering, Evaluation
Additional demo: Eigenfaces (not examinable)
Week 10
Lecture Theatre:
Mon: 
Take the Quiz: Dimensionality Reduction

Thu: 
Review: Dimensionality Reduction (and Hierarchical Clustering?)

This page is maintained by
Nigel Goddard.