1. Motivation : components of the learning problem and applications of Machine Learning. Supervised and unsupervised learning.
2. Introduction: The supervised learning problem, Classes of models, Losses, Probabilistic models and assumptions on the data. Regression and Classification.
3. When is a model good? Model complexity, bias variance tradeoff/generalization (VC dimension, generalization error), Cross Validation.
4. Models for Regression: Linear Regression, linear-in-the-parameters models, regularization.
5. Simple Models for Classification: Logistic Regression, Perceptron, Naïve Bayes Classifier
6. Kernel Methods: Support Vector Machines.
7. Random Forests
8. Neural Networks
9. Deep Learning: Convolutional Neural Networks, advanced models
10. Unsupervised learning: Cluster analysis, Linkage-based clustering, K-means Clustering.
11. Dimensionality reduction: Principal Component Analysis (PCA).
1. Introduction to Python
2. Linear models for regression and classification
3. Support Vector Machines
4. Neural Networks
5. Deep Learning with Kera