Introductory Applied Machine Learning
Course Homepage

Lecturers: Victor Lavrenko and Nigel Goddard

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 Appleton Tower, Lecture Theatre 1

Resources


Discussion Forum

The forum will contain detailed lecture notes annotated with questions and answers. It 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 schedule below.

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, Thursday, 4 p.m.
3 Week 7, Thursday, 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 Friday in Week 4 (Friday 10 Oct 2014) 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 within two weeks of the due date.

Assignment #2

The question sheet is here: a112_2.pdf [discuss]. It is due at 1600 on Thursday in Week 7 (Thu 30 Oct 2014) 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 14 Nov 2014.

Assignment #3

The question sheet is here: a112_3.pdf [discuss]. It is due at 1600 on Monday in Week 9 (Mon 10 Nov 2014) 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 28 Nov 2014.

Assignment #4

The question sheet is here: a112_4.pdf [discuss]. It is due at 1600 on Friday in week 10 (Fri 21 Nov 2014) 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 5 Dec 2013.

Please read the Informatics policy on late submissions and plagiarism.

Tutorials

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

Labs

Labs will be in weeks 3,5,7 and 9. [Groups, times and rooms]

Week-by-Week listing

(This list is subject to change.)

Week 1

Lectures: Introduction [slides] [video]
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] [pdf] [video], Mathematics [slides], Basic Probability and Estimation [slides] [video]
Tutorials: No tutorials in week 2
Readings: Textbook chapters 7.1, 7.2

Week 3

Lectures: Naive Bayes Classification [slides] [pdf] [video]
Tutorial 1: Naive Bayes and data representation [solutions]
Lab 1: Naive Bayes classification
Readings: Textbook chapters 4.2

Week 4

Lectures: Decision trees [slides] [pdf] [video], Overfitting and generalization [slides 1-10] [pdf] [video]
Tutorials: No labs or tutorials in week 4
Readings: Textbook chapters 3.2, 3.3, 4.3, 6.1, 6.5

Week 5

Lectures: Linear regression [slides] [pdf] [video], Evaluation [slides 11-29] [pdf]
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

Lectures: Logistic Regresssion [slides] [pdf] [video], Optimisation [slides] [pdf]
Regularization [slides], [pdf], Support vector machines part I [slides], [pdf] [video],
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

Lectures: Support vector machines Part II [slides] [pdf] [video], Nearest neighbor method [slides] [pdf] [video]
Readings: Textbook chapters 5, 4.7, 6.4
Tutorials: Logistic regression [solutions]
Lab 3: Support Vector Machines, Evaluation

Week 8

Lectures: K-means clustering [slides] [pdf] [video]
Readings: Textbook chapters 4.8, 6.6

Week 9

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

Week 10

Lectures: Hierarchical clustering [slides] [pdf] [video] Neural networks [pdf],
Additional demo: Eigenfaces (not examinable)

Some [video] links are from 2013. Topics may differ.

This page is maintained by Victor Lavrenko and Nigel Goddard.



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