Day 1: Machine Learning in a Nutshell, Probability Distributions and Distance Functions, Regression Analysis (Linear and Logistic), Data Visualization, Regression Analysis.
Day 2: Introduction to Classification, Dimensionality Reduction (PCA, LDA, and likes), Implementing PCA and LDA, Feature Selection in Practice.
Day 3: Introduction to Clustering, Mixture Models and EM, Clustering in Practice, Implementing EM.
Day 4: SVM with Kernel Trick, Decision Tree and CART, Classification in Practice, Implementing CART.
Day 5: Ensemble Learning (Random Forest, Bagging, Boosting), Neural Networks (MLP, AE), Implementing RF, Implementing MLP.
Day 6: Deep Neural Networks, Bayesian Learning, Implementing CNN, Implementing RNN.
The detailed course is outlined here.