INF8245E - Machine Learning

Program overview

Schedule

• Intro to ML, Linear Regression
• Linear Regression, Overfitting, ML Pipeline, Classification, k-NN, More on Regression,Gradient Descent
• Gradient Descent, Regularization, Decision theory, Empirical Risk Minimization
• Bias-Variance Tradeoff, linear classification
• GLM, GDA, Naive Bayes
• Logistic Regression, Newton-Raphson method, Evaluation Metrics, Perceptron
• Max-margin Classifiers, SVMs
• Decision trees, Ensembles: Bagging, Random Forests, Stackin
• Boosting, Neural Nets, Backpropagation
• Backpropagation, Deep Neural Networks, Convolutional Networks
• Optimization, Dimensionality Reduction, PCA, LDA
• Bayesian learning, MLE, MAP, Bayesian Linear Regressio
• Kernel Methods, Gaussian Process, Frontiers, What Next?