COURSE LAYOUT
Week 1: Mathematical Basics 1 Introduction to Machine Learning, Linear Algebra
Week 2: Mathematical Basics 2 - Probability
Week 3: Computational Basics Numerical computation and optimization, Introduction to Machine learning packages
Week 4: Linear and Logistic Regression Bias/Variance Tradeoff, Regularization, Variants of Gradient Descent, MLE, MAP, Applications
Week 5: Neural Networks Multilayer Perceptron, Backpropagation, Applications
Week 6: Convolutional Neural Networks 1 CNN Operations, CNN architectures
Week 7: Convolutional Neural Networks 2 Training, Transfer Learning, Applications
Week 8: Recurrent Neural Networks RNN, LSTM, GRU, Applications
Week 9: Classical Techniques 1 Bayesian Regression, Binary Trees, Random Forests, SVM, Nave Bayes, Applications
Week 10: Classical Techniques 2 k-Means, kNN, GMM, Expectation Maximization, Applications
Week 11: Advanced Techniques 1 Structured Probabilistic Models, Monte Carlo Methods
Week 12: Advanced Techniques 2 Autoencoders, Generative Adversarial Network
Thanks to the support from MathWorks, enrolled students have access to MATLAB for the duration of the course.