Eduardo Bezerra

Local/horário

• CEFET/RJ, por videoconferência (plataforma MS Teams)
• Dia/horário: 5as-feiras, das 13:25h às 17:00h

Overview

Aprendizado de Máquina (Machine Learning) é um campo de estudo da Inteligência Artificial cujo objeto de estudo são sistemas que podem aprender a realizar alguma tarefa por meio de experiências. Neste curso, o objetivo é apresentar uma introdução aos conceitos, modelos, métodos, técnicas e aplicações do Aprendizado de Máquina. São também apresentados alguns algoritmos pertencentes a diferentes famílias de métodos em AM (simbolistas, conexionistas, probabilísticos, baseados em proximidade).

Plano do curso

Veja o plano do curso. Veja também o calendário acadêmico das pós-graduações do CEFET/RJ. Veja ainda a página com material relevante organizado por aula.

Projects

• T1 – Due 24/mar
• T2 – Due 11/out
• T3 – Due 25/out

Additional resources

• Video: Recurrent Neural Networks, Image Captioning, LSTM, Andrej Karpathy.
• Online course: Practical Deep Learning For Coders
• Online course: Neural Networks and Deep Learning
• Online course: (Codecademy) Learn Python
• Word Embeddings
• Representation Learning for Reading Comprehension
• Practical Generative Adversarial Networks for Beginners

Readings

• (r1) Tom Mitchel, Key Ideas in Machine Learning
• (r2) Pedro Domingos, A Few Useful Things to Know About Machine Learning
• (r3) Sebastian Raschka, Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
• (r4) Eric Bauer & Ron Kohavi, An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
• (r5) Matriz Factorization for Recommender Systems
• (r6) Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Books

• Max Kuhn and Kjell Johnson, Feature Engineering and Selection: A Practical Approach for Predictive Models
• Jeremy Watt et al, Machine Learning Refined: Foundations, Algorithms, and Applications
• Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, 2019.
• Sebastian Raschka, Python Machine Learning, 3rd ed, Packt Publishing, 2019.
• Jake VanderPlas, Python Data Science Handbook, 2016.
• Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. pdf github
• Ethen Alpaydin, Introduction to Machine Learning, MIT Press, 2010.
• Ian Goodfellow et al, Deep Learning, MIT Press, 2016.
• Tom Mitchell, Machine Learning, McGraw-Hill, 1997.
• Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition edition, August 2012.
• Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data . Cambridge University Press, Cambridge ; New York, 1st edition, 2012.
• Christopher Bishop. Pattern Recognition and Machine Learning . Springer, New York, October 2007.
• Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R . Springer, 1st ed. 2013. corr. 4th printing 2014
• Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning From Data . AMLBook, S.l., March 2012.
• Brett Lantz. Machine Learning with R . Packt Publishing, Birmingham, October 2013.
• Simon O. Haykin. Neural Networks and Learning Machines . Prentice Hall, New York, 3 edition edition, November 2008.
• Ani Adhikari & John DeNero, Artificial Intelligence: Foundations of Computational Agents, second edition, Cambridge University Press, 2017.

Livros de interesse geral sobre Aprendizado de Máquina

• Gary Marcus & Ernest Davis, Rebooting AI: Building Artificial Intelligence We Can Trust, 2019.
• Melanie Mitchel, Artificial Intelligence: A Guide for Thinking Humans, 2019.
• Jimmy Soni & Rob Goodman, A Mind at Play: How Claude Shannon Invented the Information Age, 2017.
• Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, 2015.
• Nick Bostrom, Superintelligence: Paths, Dangers, Strategies, 2014.