Machine Learning general overview. Supervised and Unsupervised learning problem statements. Metrics. kNN.
Maximum Likelihood estimation. Naive Bayesian Classification.

Introduction, overview and metric algorithms

SVM, kernel trick. Linear algebra recap: eigendecomposition of a matrix, SVD. PCA

Linear classification & dimensionality reduction

Bias-Variance decomposition. Train-Validation-Test framework. Hyperparameters tuning.

Model construction and validation

Gauss-Markov theorem. L1 and L2 regularization. Matrix differentiation.

Linear regression

Margin. Logistic regression. Multiclass classification strategies.

Linear classification

Construction procedure. Bootstrap recap. Bagging. Random Subspace Method. Random Forest. Out of Bag error.

Decision trees & ensembling methods

Stacking. Blending. Gradient boosting.

Ensembling methods

Feature engineering and missing values. Feature importance estimation.

Midterm

Motivation & timeline. Intuition, forward pass. NN specific terminologyBackpropagation mechanism.  Activation functions.

Intro to Deep Learning

SGD refinements. Weights initialization. NN overfitting and regularization methods.

Optimization & regularization in Deep Learning

Recurrent neural networks, sequence modeling. Vanishing gradient problem.

Deep learning for structured data

Convolutional layers. Upconvolutions. Pooling. Most influential architectures overview.

Deep Learning for structured data 

Text vectorization. Autoencoders. Embeddings.

Embeddings

Manifold learning. Dimensionality reduction. Clustering algorithms.

Unsupervised learning

Final test 

General recap. Extra themes.