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ADVANCED TOPICS IN BIOINFORMATICS

Course: BBS741 - Advanced Topics in Bioinformatics
Semester Offered: Fall 2020, Fall 2022
Tuesdays, 9:00-11:00am

Professor:
Zhiping Weng, Zhiping.Weng@umassmed.edu

Course Topics:

  • Introduction to machine learning
  • Unsupervised learning: clustering
  • Unsupervised learning: Principal component analysis
  • Unsupervised learning: t-SNE and UMAP
  • Project for unsupervised learning techniques
  • Linear regression
  • Classification
  • Resampling methods
  • Project for linear learning techniques
  • Regularization
  • Decision trees, bagging, and random forest
  • Neural networks
  • Deep Learning
  • Project for nonlinear learning techniques
  • Expectation Maximization
  • Hidden Markov models
  • Project for graphical learning techniques
  • Project presentations

Course prerequisites:
This course is designed for graduate students who are interested in pursuing Bioinformatics and Computational Biology research. Previous programming experience, particularly in Python, is highly recommended. This course will cover a range of topics in the fields of statistical and machine learning so prior coursework in linear algebra or statistics is also recommended.

Course materials:
Students will be required to have a laptop computer with access to Python. Instructions for required package installation will be sent prior to each assignment

Lectures will draw from the following textbooks:

Python resources:

https://www.pythonforbeginners.com/files/reading-and-writing-files-in-python
https://stackoverflow.com/questions/9039961/finding-the-average-of-a-list
https://stackoverflow.com/questions/35966940/finding-the-max-of-a-column-in-an-array
https://jakevdp.github.io/PythonDataScienceHandbook/02.04-computation-on-arrays-aggregates.html

Grading:
There will be one homework assignment, four projects and one final presentation for this course. Final grades will be calculated as follows

Homework #1 - Python & Rosalind problems 10%
Project #1- Clustering, PCA, & t-SNE 20%
Project #2- Linear models  20%
Project #3- Non-linear models 20%
Project #4- Graphical learning  20%
Final presentation - Application to research  10%