The morning sessions will consist of lectures and small
practical exercises introducing the different algorithms, and the afternoon sessions
will consist of programming exercises where
the algorithms will be implemented.
The main programming language will be Python
and all program templates provided in the course will be written in Python.
Prior detailed knowlegde of Python programming is strongly encouraged but NOT required.
However, basic programming skills are required to follow the course.
BACKGROUND TEXTS - Data redundancy reduction
- 9.00 - 9.15
- Questions to yesterdays lectures and exercises
- 9.15- 9.45
- Data redundancy reduction algorithms (Hobohm1 and Hobohm2) [PDF]. [PPTX]. (MP4).
- 9.45 - 12.00
- Hobohm redundancy reduction algorithms
- Answers to Hobohm programming exercise
- 12.00 - 13.00
- Lunch
- 13.00 - 13.30
- Answers to Fridays CNN and todays Hobohm exercises
- 13.30 - 13.45
- Doing things in C - A few examples: Alignment and ANN training
- Some code examples
- 13.45 - 14.30
- Selection of projects, formation of project groups and start of project work Document for project signup.
- NOTE, EACH GROUP MUST SENT AN EMAIL TO MORTEN (morni@dtu.dk) WITH THE PROJECT TITLE, GROUP MEMBERS (NAME AND ID), A BRIEF PROJECT OUTLINE, AND A DESRIPTION OF THE PROJECT DATA AND HOW THESE ARE LOCATED BEFORE TUESDAY JUNE 18TH, 11.59 AM.
- 14.30 - 17.00
- Work on project (on your own)
Tuesday 18. - Monday 24. June. Project work
- No lectures. Project work
- Projects must be submitted (in PDF format) via DTU Learn Monday 24. of June 11.59 AM (just before lunch)
Tuesday, 25. June, Exam
The written exam will be available at the course page
Tuesday 25th of June at 9.59, and needs to be handed in
via DTU Learn Tuesday 25th of June before 12.00. The exam is open
book, meaning that you can seek information at the internet.
It is allowed to use generative AI (e.g., chatGPT) but please note:
-
Make sure that you have read Study announcement s1885: Use of Artificial Intelligence (generative AI) in teaching and exams at DTU
-
Importantly: "Always explicitly declare if any part of your submission is not your original work. Failure to do so may be considered exam cheating, in accordance with DTU's code of honour and the rules for referencing when using generative AI"
- More generally: generative AI is a tool that can (and should) be used to improve productivity much like other tools and sources of information. If you do use chatGPT to get information or write text, then you should (1) declare when you have done so, as specified above, (2) make sure the information is correct and relevant for the question being asked, (3) possibly rephrase, to get better and more correct responses.
- In my experience, chatGPT responses to previous exam questions have not been very good, often missing the point in my questions.
You however cannot consult anyone, and plagiarism is
seriously condemned. The exam will take place in the usual class room in building 210.
exam.ipynb (NoteBook)
exam.html (HTML)
Wednesday 26. June
Project evaluation
Each group has 10 minutes to present their project followed by 5 minutes for
questions. Note, only the group will be present for the presentation of
the individual projects.
The project presentations will take place in the usual class room in building 210.
Wednesday 26st of June, 8.30 - 12.00
8.30 - 8.50
Group 1
Christian Christensen (s223096), Mikkel Piester Hansen (s223105)
Peptide MHC binding predictions using artificial neural networks with different sequence encoding schemes
8.50 - 9.10
Group 2
Jiawen We (s222372), Magnus Harthimmer (s233426), Rune Daucke (PhD)
Implementation of HMM Baum-Welch algorithm
9.10 - 9.30
Group 3
Matilde Uth (s195796), Jacqueline Printz (s194377), Christina Christiansen (s223094)
Comparative study of PSSM, ANN, SMM for peptide MHC binding
9.30 - 9.50
Group 4
Paula Gomez-Plana Rodriguez (s233165), Maria Gabriela Frascella Bracho (s233113), Eirini Giannakopoulou (s230204), Amanda Jimenez (s233150)
Comparative study of PSSM, ANN, SMM for peptide MHC binding
9.50 - 10.00
Break
10.00 - 10.20
Group 5
Johanne Lund (s233246), Luisa Weisch (s233028), Eleni Tseperi (s240066)
Comparative study of PSSM, ANN, SMM for peptide MHC binding
10.20 - 10.40
Group 6
Anton Wang Strandberg (s183220), Ona Saulianskaite (s232958), Johan von Staffeldt (s225001)
Tools for ANN training (FFNN + CNN)
10.40 - 11.00
Group 7
Emilie Sofie Engdal (s194360), Sarah Rosenberg (s194689), Asger Bjoern Larsen (s204306), Saxe i Dali Wagner (s204559)
Implementation of regularization in ANN training
11.00 - 11.20
Group 8
Lea Eschen Skytthe (s203531), Trine Soegaard (s204655), William Hagedorn-Rasmussen (s194545)
Peptide MHC binding predictions using artificial neural networks with different sequence encoding schemes
11.20 - 11.40
Group 9
Anas Majed El-Youssef (s233381), Xavier Vinas Margalef (s233532), Max Edin (maxed@dtu.dk)
Method evaluation using crossvalidation
11.40 - 12.00
Group 10
Peptide MHC binding predictions using artificial neural networks with different sequence encoding schemes
Balint Norbert, s204668,Rebecca Hjermind Millum, s215024, Grinos (phd)
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