Part 1 - Introduction to Machine Learning
Introduction, linear regression
Python libraries: pandas, numpy, visualization libraries
Evaluation, training and test sets, ROC curves
Decisions trees
Part 2 - Data science and Statistics
Density estimation, MLE, Bayes classification
Clustering, PCA
Statistics for scientists: correlations, p-values and multiple testing
A mini project in analyzing high throughput data
Part 3 - Big Data
Introduction to Hadoop
Query Languages
Machine Learning use cases over Big Data
Part 4 - A next step in Machine Learning
Classifiers - SVM and kNN
A brief introduction to Deep Learning
Part 5 - Presentation of the mini project results
Further details here: http://kereno.com/syllabus_wis.pdf