Homework

Presentation

Lecture Notes

Reference

Lecture notes

Week 1 Lecture 1: Introduction

Lecture 2: Basics of linear algebra and bioinformatics

Week 2 Lecture 3: Clustering: Basics and Algorithms

Lecture 4: Clustering: Graph based clustering

Week 3 Lecture 5: Biclustering

Week 4 Lecture 6: Classification: Basics and Algorithms

Lecture 7: Classification: Linear Discriminant Analysis

Week 5 Lecture 8: Classification: Support Vector Machines (linear case)

Lecture 9: Classification: Support Vector Machines (nonlinear case)

Week 6 Lecture 10: Semi-supervised clustering

Lecture 11: Semi-supervised clustering

Week 7 Lecture 12: Semi-supervised Classification

Lecture 13: Semi-supervised Classification

Week 8 Lecture 14: Feature reduction: PCA and kernel PCA

Lecture 15: Feature reduction: LDA, CCA, and kernel CCA

Week 9 Lecture 16: Kernel Methods: Basics

Lecture 17: Kernel Methods: Diffusion kernel and string kernel

Week 10 Lecture 18: Kernel Methods: String kernels for protein sequence

Lecture 19: Kernel Methods: Multiple kernel learning

Week 11 Lecture 20: Student presentation

Lecture 21: Student presentation

Week 12 Lecture 22: Student presentation

Lecture 23: Student presentation

Week 13 Lecture 24: Student presentation

Lecture 25: Student presentation

Week 14 Lecture 26: Manifold learning: MDS and Isomap

Lecture 27: Manifold learning: LLE and LTSA

Week 15 Lecture 28: Manifold learning: Laplacian Eigenmaps

Lecture 29: Manifold learning: A unified view and Nystrom’s method

Week 16 Lecture 30: Overview and research topics