Lecture series on Machine Learning and Deep Learning

Start date: March 5 2018.
End date: April 5 2018.

Venue: Proin Room (DFTE-UFRN).

Instructors: Prof. Luciano Casarini, Prof. Askery Canabarro.

Class on Mondays and Wednesdays, at 4:00 pm.

Course Plan

First part: (Prof. Luciano Casarini)

1. Intro to ML: what is ML, why? when? Types of ML, main challenges of ML. Resources;
2. Example of ML: python, jupyter, pandas, matplotlib, prepare and visualize the data, train an test, cross-validation. Scikit-learn;
3. Classification: MNIST, confusion matrix, precision and recall, ROC curve, errors analysis;
4. Regression: linear regression, gradient descent, regulatization, logistic regression;
5. Support Vector Machine: linear and non linear SVM classification, SVM regression, Kernels;
6. Decision trees: CART, Gini impurity and entropy, regularization and instability;
7. Ensemble Learning and Random Forest: voting classifiers, bagging and pasting, Random Forest, Boosting, Stacking;
8. Dimensionality Reduction: Projection, Manifold Learning, Principal Component Analysis, Kernel PCA.

Second part: (Prof. Askery Canabarro)

1. Tensor Flow;
2. Introduction to Artificial Neural Network;
3. Training Deep Neural Nets;
4. Distributing Tensor FLow Acorss Devices and Servers;
5. Convolutional Neural Network;
6. Recurrent Neural Network;
7. Autoencoders;
8. Reinforcement Learning.

Bibliography and suggested reading:

Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems.2017.

Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. 2016.

Sebastian Raschka. Python Machine Learning. 2017.

Geoffrey Hinton. Neural Networks for Machine Learning (Online Course with certificate).

Trevor Hastie, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: data mining, inference, and prediction. 2016.

Additional Material;

Quora;

Reddit;

Kaggle;

GitHub.