
Computational Biology
Course No: BIOL6385.001.18S / BMEN6389.001.18SSpring 2018 Biology Department, The University of Texas at Dallas Instructor : Michael Zhang Director, Center for Systems Biology, The University of Texas at Dallas Associate instructor: Pradipta Ray Research Scientist, Center for Systems Biology, The University of Texas at Dallas 
  

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 notsoshort 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 
KarlinAltschul 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. BaumWelch 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  
Last date to withdraw with "W" grade (graduate students)  
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 CommentsSVM 

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 