# List of Readings

This list is the same as in the course description. However, we will be update this as we go along in the course, with your own suggestions for relevant papers also included.

Please note that these are suggested readings. You are not expected to read all of them. Readings with * are highly recommendable, and those with ** are to be presented by YOU! (refer to 'Presentations' for details and schedule)

Note: Readings that do not have a link either refer to the books or must be googled.

# April 8 - Exam 1

**Manifold Learning, Apr 22**

**Novelty Detection, Apr 22**

**Probabilistic Modeling, Apr 23,29**

- Information Theory, Inference and Learning Algorithms, David MacKay, Chapters 2 and 3
- Learning with Kernels Chapter 6
- |Pattern Recognition and ML, Bishop - Ch 8 Graphical Models

# April 30 - Project Progress Report 2

**Active learning, Apr 30**

- Fast Kernel Classifiers with Online and Active Learning
- Summary of current work on Active Learning
- (Liina) Active Learning in the Drug Discovery Process
- Query Learning with Large Margin Classifiers

** Fisher Kernel, May 6**

- Learning with Kernels Chapter 15
- Fisher Discriminant Analysis with Kernels
- Using the Fisher kernel method to detect remote protein homologies
- Asymptotic properties of the Fisher kernel

** Data Fusion, May 7 **

- Kernel-based data fusion and its application to protein function prediction in yeast
- (Priit) Kernel-based data fusion for gene prioritization

** Multi-task Learning, May 13 **

** KCCA, May 14 **

- Roland will talk about KCCA
- Remainder of May - Biological applications and Project discussions

# May 27 - Final Project Paper

# May 27,28 - Project Presentations

# **** First half below ****

**Introduction, Feb 12**

*Kernel Methods for Pattern Analysis*, Chapter 1- The Discipline of Machine Learning, Mitchell
- Machine Learning, Dietterich
- Intro to Machine Learning, Nilsson
- ML and Pattern Recognition (slides), LeCun

**Optimization, Feb 13**

- Practical Optimization: A Gentle Introduction*
- Introduction to Optimization Methods: a Brief Survey of Methods **
- The Interplay of Optimization and Machine Learning Research*
- Learning with Kernels,
*Chapter 6*

**Kernel Methods, Feb 19,20**

- Kernel Methods for Pattern Analysis (Cristianini & Shawe Taylor),
*Part I deals with the theory behind Kernels, Part III is a plethora of kernel types. Refer as necessary* - Learning with Kernels (Scholkopf & Smola),
*Chapter 2* - An Introduction to Kernel-Based Learning Algorithms *,
*Some of this reading is related to the next lecture* - Fast String Kernels using Inexact Matching for Protein Sequences **

**Support Vector Machines, Feb 26,27**

- Support Vector Machines: Hype or Hallelujah
- Statistical learning and kernel methods
- Tutorial on Support Vector Machines for Pattern Recognition*
- SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition**

**Faster SVMs, Mar 5 - Presented by Konstantin**

**Support Vector Regression, Mar 11**

- A tutorial on Support Vector Regression Regression *
- Duality, Geometry and Support Vector Regression
- Statistical Analysis of Semi-Supervised Regression **
- Compressed Regression

**Ranking + Semi-supervised Regression, Mar 12**

**Nu SVMs + SMO, Mar 18**

**Evaluative Methods, Mar 19**

- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach**
- Crafting Papers in Machine Learning*
- Regression Error Characteristic Curves
- Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria

**Boosting, Mar 25**

- AdaBoost
- A short introduction to boosting*
- An efficient boosting algorithm for combining preferences**

**Principal Component Analysis, Mar 25**

- Kernel Methods for Pattern Analysis, Ch 6, Section 6.2
- A tutorial on Principal Component Analysis*
- The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data**

**Clustering and Spectral Clustering, Apr 1,2,15**

- Towards a statistical theory of clustering
- Support Vector Clustering
- A sober look at clustering stability**
- Comparing Clusterings
- On spectral clustering: Analysis and an Algorithm (ps)
- A Comparison of Spectral Clustering Algorithms* (ps)
- Functional Grouping of Genes Using Spectral Clustering and Gene Ontology**