Consult this page for class, recitation and exam dates, handouts, and solutions.

A printable version of course policy and syllabus is here. Updates to this document will be made on this website.

Slides will be updated after class. Students can view old versions before updated.

## Course Schedule

The last day to drop the course without a "W" grade is Jan 24. The last day to drop a graduate course in any way is Mar 26. See the academic calendar for details.

Lecture Topic Handouts / readings NB

Unit 1: Background and Statistical Inference

Unit 1
Class 1
Introduction Slides CF gene discovery original paper, hosted at UNC here
Mathematical Writing

See R tutorial here, PERL tutorials here, Python tutorials here, and MATLAB tutorial here.

A database of open source machine learning tools is at mloss.org, here.

The not-so-short introduction to LATEX.
Unit 1
Class 2
Introduction (cont'd):Probability Theory
Class notes
Unit 1
Class 3
Introduction (cont'd) Statistical inference
Slides
Unit 1
Class 4
Bayes Nets I : Modelling and Estimation
Dirichlet Notes

EM Paper
Unit 1
Class 5
Bayes Nets II : Bayes net/Inference
Slides
Bayesian Inference paper
Last day to drop course without a "W" grade.

Unit 2: Sequence Alignment

Unit 2
Class 1
Alignment I. Scoring Models
Slides HW1 out
HWK1

HWK1 Solutions
Unit 2
Class 2
Alignment II. Dynamic Programming and Global Alignment
Slides
Unit 2
Class 3
Alignment III. Local alignment and heuristics

Random Path Analysis

Square Functional equation handout 1

Square Functional equation handout 2
Unit 2
Class 4
Karlin-Altschul Statistics and Score Significance
Local Alignment Handout

Alignment Score Significance

Unit 3: Markovian Models

Unit 3
Class 1
Markov Nets I. Markov Chain
Slides

Handout
Unit 3
Class 2
Markov Nets II. HMM:Segmentation

Slides

FB Algorithm Derivation

HWK 1 due

HWK2

HWK Solutions
Unit 3
Class 3
Markov Nets III. HMM: Viterbi, Forward/Backward

Viterbi Algorithm Handout

HMM handout
Unit 3
Class 4
HMM : Markov Nets IV. Baum-Welch algorithm
Pair HMM
Unit 3
Class 4
Markov Nets V. Profile HMM
Profile HMM
Midterm Exam Review
HW2 due
Midterm exam (in class) Midterm Solutions
Unit 3
Class 5
Markov Nets VI. HMM vs CRF
Slides

Slides_HMM vs CRF

Handout

Unit 4: Comparative Genomics and Evolution

Unit 3
Class 6
Evolutionary models I Slides
Spring break, no class

Spring break, no class

Unit 4
Class1
Evolutationary Models II
Unit 4
Class 2
Phylogenetic Trees I
Slides

Handout
Unit 4
Class 3
Phylogenetic Trees II HWK3

Unit 5: Motif finding

Unit 4
Class 4
Motif finding (Greedy, EM, Gibbs sampling) Slides
Unit 5
Class 1
Evaluation of significance of motifs Slides
Unit 5
Class 2

Discriminant motif finding (DWE/DME)

Functional motif finding (Regression, CART, MARS)

Unit 6: Machine Learning

Unit 5
Class 3
SVM and Kernel method

ML Introduction

SVM

SVM_2017

Unit 5
Class 4
Ensemble learning, Boosting (Random Forest) Slides Slides_2017
Unit 5
Class 4
Lasso, Sparsity, Regularization
Ridge_regression

Lasso

Handout
Notes
Unit 6
Class 1
Deep Learning tutorial Slides
Unit 6
Class 2
Deep Learning In Computational Biology
DeepBind

DeepSea

DeepVariant

DeepLearning_ISL
HW3 due
HWK3_solution
Unit 6
Class 3
Final Exam review
Final exam in class

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