Online tutor for Machine Learning and Algorithms for Genomics course for almost blind bioinformatics graduate student needed
I am a severely visually impaired student. My eyesight is not good enough to see the projector in class but all the class material is freely available online.
To access the course material for this class, please follow the steps below:
1) Go to the course website, which is https://moodle.hu-berlin.de/course/view.php?id=71703
2) Switch language to English by clicking on the fourth pull-down menu from the left (= most right pull down menu), which is shown with white font on dark blue background. It may be set to German since it is a German website. Then it would say at this pull down menu “Deutsch (de)’. Just click onto the white arrow to the right and select English (en).
3) The course name should read: “Machine Learning and Algorithms for Genomics”
4) Go to the bottom of the page it says Guest access
5) In the box, above which it says Guest access key, type the access key for this course, which is MLGenomics16
6) Press on the Submit button at the bottom of the page and you can see all the course materials.
7) If you are having trouble accessing the course material but would like to take a look at it in order to decide whether you’d like to teach it to me please contact me so that we can access the course website remotely together.
In case you feel it is too complicated to access the course material below is the syllabus of what will be covered in this course:
1) Week 1: Introduction • Classification + background (probability, biology)
2) Week 2: Probabilistic, generative models • Weight matrices and other sequence motif models • Chomsky hierarchy and stochastic grammars: Markov chain models • Hidden Markov Models: parsing + applications (gene finding, protein domains: annotation of genomes & proteomes)
3) Week 3: Generative models (cont.) • HMMs: training + applications • Motif finding: overview & probabilistic approaches (EM & Gibbs sampling) • Enumerative algorithms
4) Week 4: Deep sequencing data • Data structures and read alignments • Peak calling, transcript annotation & gene expression 21 Syllabus
5) Week 5: From generative models to probabilistic networks • RNA structure prediction & RNA genes • Gene expression networks, hierarchical Bayes
6) Week 6: Discriminative approaches I • Support Vector Machines: theoretical foundations • SVMs: applications and sequence kernels • Random Forests
7) Week 7: Discriminative approaches II • Dimensionality reduction & unsupervised learning • Deep learning
My math, statistics and programming knowledge is very limited since I am a molecular biologist by training. I just recently had to switch to bioinformatics because my vision had worsened to a point that I could no longer perform wet-lab work efficiently under time pressure. That is why I am looking for a very patient tutor, who is willing to take the time to teach me how to apply all the concepts taught in this course and who could potentially assist me in writing a proposal about how to apply machine learning techniques to make new discoveries
I thank you very much in advance for considering taking your time to assist me in learning the skills and techniques needed for earning a PhD in bioinformatics despite me being almost blind.
I am very much looking forward to discuss more details with you soon.