BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics
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1 Course Director: Dr. Kayvan Najarian (DCM&B, Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm Palmer Commons Bldg. Lecture (1): Introduction to Machine Learning Time: September 9 (Wednesday), 9:00 10:30 AM Topics: The concept of machine learning (ML) is presented and the need of ML in systems biology and clinical informatics are briefly discussed. Lecture (2): Mathematical and Statistical Foundations Time: September 14 (Monday), 9:00 10:30 AM Topics: Some computational methods in linear algebra and probability theory are briefly reviewed. Lecture (3): A Brief Overview of Machine Learning Time: September 16 (Wednesday), 9:00 10:30 AM Topics: Different types of learning, including supervised, unsupervised, reinforced and active learning are briefly discussed. Measures to explore the accuracy and reliability of ML models are introduced. Lab (1): Introduction to MATLAB Time: Sept 16 (Wednesday) 10:30 11:30 AM Topics: Basic operations in MATLAB, using MATLAB for manipulation of matrices and functions, graphical representation of data using MATLAB is presented. Lecture (4): Unsupervised and Reinforced Learning Time: September 21 (Monday), 9:00 10:30 AM Topics: Some of the main unsupervised/clustering methods, including K-means and Gaussian Mixture Models are discussed. Also, a brief discussion of reinforced learning and its applications are provided. Lecture (5): Unsupervised Learning; K Nearest Neighbors and Bayesian Classifiers Time: September 23 (Wednesday), 9:00 10:30 AM Topics: Different types of simple supervised methods, including K Nearest Neighbor and some Bayesian methods are reviewed. 1
2 Lecture (6): Unsupervised Learning; Decision Trees Time: September 28 (Monday), 9:00 10:30 AM Topics: A brief introduction to the structure and applications of decision trees is given. Some of biomedical and biological applications of supervised learning are explored. Lecture (7): Unsupervised Learning; Classification and Regression Trees (CART) & Random Forest Time: September 30 (Wednesday), 9:00 10:30 AM Topics: A brief description of a commonly-used regression tree, CART, and the use of many trees in Random Forest, is given. Some applications in systems biology and clinical informatics are explored. Lab (2): Exploring WEKA for Unsupervised Learning Time: September 30 (Wednesday) 10:30 11:30 AM Topics: Using WEKA, decision tree and Random Forest are applied to a couple of applications. Lecture (8): Regression I Time: October 5 (Monday), 9:00 10:30 AM Topics: A brief introduction to basic linear regression methods is provided. Lecture (9): Regression II Time: October 7 (Wednesday), 9:00 10:30 AM Topics: A brief introduction to sparse linear regression methods and their applications is provided. Lecture (10): Logistic Regression Time: October 12 (Monday), 9:00 10:30 AM Topics: The basics of Logistic regression and sparse logistic regression are discussed. Lecture (11): Support Vector Machines Linear Kernels Time: October 14 (Wednesday), 9:00 10:30 AM Topics: A brief description of linear support vector machines is given. Lab (3): Support Vector Machines Non-linear Kernels Time: October 19 (Wednesday), 10:00 11:30 AM Topics: Using LIBSVM, SVM is applied to a couple of applications. 2
3 Lecture (12): Ensemble Learning I Time: October 21 (Wednesday), 9:00 10:30 AM Topics: Some basics of ensemble learning are discussed. A brief introduction to bagging is given. Lab (4): Exploring Logistic Regression Time: October 21 (Wednesday), 10:30 11:30 AM Topics: Using SLEP, Logistic regression is applied to analyze Alzheimer s disease data.s. Lecture (13): Ensemble Learning II Time: October 26 (Monday), 9:00 10:30 AM Topics: A brief introduction to boosting is given. Lecture (14): Dimensionality Reduction Time: October 28 (Wednesday), 9:00 10:30 AM Topics: Basic dimensionality reduction methods such as PCA and ISOMAP are discussed. Lab (5): Exploring SVM Time: October 28 (Wednesday), 10:30 11:30 AM Topics: Using LIBSVM, SVM is applied to a couple of applications Lecture (15): Application in Networks for Systems Biology Time: November 2 (Monday), 9:00 10:30 AM Topics: Signaling network/cascade inference. Lecture (16): Clinical Informatics I Time: November 4 (Wednesday), 9:00 10:30 AM Topics: Integrating Genetic, Clinical, Image information to clinical outcome, applications in Alzheimer s disease. Lecture (17): Clinical Informatics II Time: November 9 (Monday), 9:00 10:30 AM Topics: Biomarker selection and drug response prediction in cancers and other diseases. Lecture (18): Chemical Informatics Time: November 11 (Wednesday), 9:00 10:30 AM Topics: Application of text mining and random forest in chemical informatics 3
4 Lab (6): Implementing a Best-performing Algorithm Time: November 11 (Wednesday), 10:30 11:30 AM Topics: Implementing the best-performing algorithm in 2014 DREAM Alzheimer s Disease Challenge- Sub 1. Lecture (19): Multi-task Learning Time: November 16 (Monday), 9:00 10:30 AM Topics: A brief introduction to multi-task learning and its applications is given. Lecture (20): Transfer Learning Time: November 18 (Wednesday), 9:00 10:30 AM Topics: A brief introduction to transfer learning and its applications is given. Lab (7): Exploring Multi-task and Transfer Learning Time: November 18 (Wednesday), 10:30 11:30 AM Topics: Using MALSAR, multi-task learning is applied to a couple of applications. Lecture (23): Active Learning Time: November 23 (Monday), 9:00 10:30 AM Topics: A brief introduction to active learning and its applications is given. No classes on Nov 25 (Thanksgiving Holidays) Lecture (21): Big Data Analytics I Time: November 30 (Monday), 9:00 10:30 AM Topics: Major challenges of using machine learning within Big Data framework, in particular when dealing with clinical applications, are discussed. Lecture (22): Big Data Analytics II Time: December 2 (Wednesday), 9:00 10:30 AM Topics: Some Big Data solutions and approaches are introduced. Lecture (23): Deep Learning Time: December 7 (Monday), 9:00 10:30 AM Topics: A brief introduction to deep learning is given. Some deep learning methods and tools are introduced. 4
5 Lecture (25): Natural Language Processing I Time: December 9 (Wednesday), 9:00 10:30 AM Topics: TBA Lecture (26): Natural Language Processing II Time: December 14 (Monday), 9:00 10:30 AM Topics: TBA 5
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