Course Identification

Basic programming course + basic calculus and linear algebra

Part 1 - Introduction to Machine Learning 

Introduction, regression and classification problems  
Linear regression 
Evaluation, training and test sets, ROC curves
Decision Trees, Random Forests
Linear Classifiers, Support Vector Machines
Unsupervised Learning: Dimensionality reduction, Clustering

Python libraries: pandas, numpy, visualization libraries, sklearn 
 

Part 2 - Data Science and Statistics 

Density estimation, MLE, Bayes classification
Statistics for scientists: correlations, p-values, and multiple testing
Advanced statistical methods: non-parametric tests, 
A mini project in analyzing high throughput data

Part 3 - Class Workshops

NGS pipelines and Data Analysis

Part 4 - Presentation of the mini project results

Upon successful completion of this course students should be able to:

- understand machine learning algorithms and apply them to data 

- statistically assess observations in data including correlations

- understand and configure machine learning packages including Deep Learning and SVM

- analyze large volumes of experimental data and present results   

• An introduction to statistical learning by James and co.

• Pattern recognition and machine learning by Bishop and co.