Introductory Applied Machine Learning
Course Homepage

Lecturers: Victor Lavrenko and Nigel Goddard
TA: Boris Mitrovic
Course reps: Petar Stefanov s1141453@sms, Matthew Gould s1322005@sms

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 9 course intended for MSc students and 3rd year undergraduates.

For an overview of the planned course topics, see the Course Catalog entry and the 2011 introductory handout.

Lectures

14:10-15:00 Mondays and Thursdays in Teviot Lecture Theatre (Doorway 5), Medical School Teviot.

Resources


Discussion Forum

Please ask all questions on the discussion forum. This forum is monitored by both lectures and the TA, so if you ask questions here, you are likely to get a much faster response than if you email the lecturers individually. (However, if you have issues that should be kept confidential, then of course please do email the course lecturers.)

Assignments

There will be four equally-weighted assignments together worth 25% of the mark for the course. These will be released, and submissions due, according to the following schedule.

Assignment Released Due
1 Week 3, Monday, 4 p.m. Week 4, Friday, 4 p.m.
2 Week 5, Thursday, 4 p.m. Week 7, Wednesday, 4 p.m.
3 Week 7, Wednesday, 4 p.m. Week 9, Monday, 4 p.m.
4 Week 9, Monday, 4 p.m. Week 10, Friday, 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: a112_1.pdf [discuss]. It is due at 1600 on Fri 11 Oct 2013 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
Marks will be returned by 1600 on Fri 1 Nov 2013.

Assignment #2

The question sheet is here: a112_2.pdf [discuss]. It is due at 1600 on Wed 30 Oct 2013 by manual submission to ITO and electronic submission (please see question sheet for detailed submission instructions). The data files are here: train_auto_partA.arff, train_auto_partA_base.arff, train_auto_partB_numeric.arff, train_auto_partB_full.arff
Marks will be returned by 1600 on Fri 15 Nov 2013.

Assignment #3

The question sheet is here: a112_3.pdf [discuss]. It is due at 1600 on Tue 12 Nov 2013 by manual submission to ITO and electronic submission (please see question sheet for detailed submission instructions). The data files are here: train_images_partA.arff, valid_images_partA.arff, train_images_partB.arff, valid_images_partB.arff, test_images_partB.arff. The website where you can view the images from the validation dataset is here.
Marks will be returned by 1600 on Fri 29 Nov 2013.

Assignment #4

The question sheet is here: a112_4.pdf [discuss]. It is due at 1600 on Fri 22 Nov 2013 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.


Marks will be returned by 1600 on Fri 6 Dec 2013.

Please read the Informatics policy on late submissions and plagiarism.

Tutorials

Tutorials will be in weeks 3, 5, 7 and 9.

Lab Classes

Labs will be in weeks 3,5,7 and 9.

Week-by-Week listing

(This list is subject to change.)

Week 1

Lectures: Introduction [slides], [handout], mathematical preliminaries [slides], [handout], Basic Probability and Estimation [slides],[handout]
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

Lectures: Thinking about Data [slides] [discuss].
Tutorials: No tutorials in week 2
Readings: Textbook chapters 7.1, 7.2

Week 3

Lectures: Naive Bayes Classification [slides] [discuss], Decision trees [slides] [discuss].
Lab 1: Naive Bayes classification Tutorial 1: Naive Bayes and data representation [solutions]

Week 4

Lectures: Generalization, overfitting and evaluation [slides] [discuss].
Tutorials: No labs or tutorials in week 4

Week 5

Lectures: Nearest neighbor method [slides], [discuss]. Linear regression [slides] [discuss]
Tutorial 2: Decision Trees and kNN [solutions]
Lab 2: Attribute selection and Regression

Week 6

Lectures: Logistic regresssion [slides], [discuss], perceptrons [slides], [discuss], optimisation [slides], [discuss]

Week 7

Lecture: (no lecture on Thursday) Support vector machines part I [slides], [discuss] Regularization [slides], [discuss]
Tutorials: Logistic regression [solutions]
Lab 3: Support Vector Machines, Evaluation
Readings: SVM handout

Week 8

Lectures: Support vector machines Part II [slides], K-means clustering [slides] [discuss]

Week 9

Lectures: Mixture models and the EM algorithm [slides] [discuss], Dimensionality reduction [slides] [discuss],
Tutorials: SVMs, Clustering [solutions]
Lab 4: PCA, Clustering, Evaluation

Week 10

Lectures: Hierarchical clustering [slides] [discuss],
Additional demo: Eigenfaces (not examinable)

This page is maintained by Victor Lavrenko and Nigel Goddard.



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