Computer Science - Informatica

The course is an introduction to the basic toolkit of data analysis and machine learning using the Python programming language. Covered topics include: basics of digital image processing (8 hours); regression (6 hours); classification with discriminative and generative models (16 hours); optimization (8 hours); bias/variance (6 hours); regularization (5 hours); clustering (3 hours); dimensionality reduction (3 hours); introduction to neural networks (5 hours). For more information see the course website: https://sites.google.com/di.uniroma1.it/fds-2022-2023

Calculus and Linear Algebra, including taking derivatives, understanding matrix vector operations and notation Basic Probability and Statistics, including basics of probabilities, gaussian distributions, mean and standard deviation

Data Science: Bertsimas, O'Hair, Pulleyblank. The Analytics Edge. Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press (available at: http://infolab.stanford.edu/~ullman/mmdsn.html) Machine Learning: Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning Deisenroth, Faisal, Ong, 2020. Mathematics for Machine Learning (available at: https://mml-book.github.io/) Deep learning: Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/) Zhang Lipton Li Smola Book, 2019 Dive into Deep Learning (interactive book and code at: http://d2l.ai/index.html) Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/) Image Analysis and Recognition, Computer Vision: Richard Szeliski, 2010. Computer Vision: Algorithms and Applications (available at: http://szeliski.org/Book) Python Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html) Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook) Online Python tutorials: https://docs.python.org/3/tutorial/ Other recommended books: - Kleinberg, Tardos. Algorithm Design. Addison Wesley. - Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.

Traditional frontal lectures

Please refer to the degree course regulations.

Exam: 1) Theory: 50% (written) 2) Practise: 50%, of which - 2/3 from assignments in Python, to be submitted by given deadlines during the course - 1/3 from a final project and presentation Theory: - written exam with open-ended questions - duration: 30 minutes Assignments: - The assignments and the final projects must be submitted in groups of size [ 3 – 5 ] Final project: - Algorithms, objectives and topics for the final project may be freely chosen - Ideas for projects and resources for it would be discussed in class

More references for Data Science: - Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press. (available at: http://infolab.stanford.edu/~ullman/mmdsn.html) For Deep Learning: - Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/) - Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/) Other recommended books: - Kleinberg, Tardos. Algorithm Design. Addison Wesley. - Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press. Book reference for Python: - Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html) Online tutorials for Python: - https://docs.python.org/3/tutorial/

Traditional frontal lectures

The course is an introduction to the basic toolkit of data analysis and machine learning using the Python programming language. Covered topics include: basics of digital image processing (8 hours); regression (6 hours); classification with discriminative and generative models (16 hours); optimization (8 hours); bias/variance (6 hours); regularization (5 hours); clustering (3 hours); dimensionality reduction (3 hours); introduction to neural networks (5 hours). For more information see the course website: https://sites.google.com/di.uniroma1.it/fds-2022-2023

Calculus and Linear Algebra, including taking derivatives, understanding matrix vector operations and notation Basic Probability and Statistics, including basics of probabilities, gaussian distributions, mean and standard deviation

Data Science: Bertsimas, O'Hair, Pulleyblank. The Analytics Edge. Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press (available at: http://infolab.stanford.edu/~ullman/mmdsn.html) Machine Learning: Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning Deisenroth, Faisal, Ong, 2020. Mathematics for Machine Learning (available at: https://mml-book.github.io/) Deep learning: Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/) Zhang Lipton Li Smola Book, 2019 Dive into Deep Learning (interactive book and code at: http://d2l.ai/index.html) Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/) Image Analysis and Recognition, Computer Vision: Richard Szeliski, 2010. Computer Vision: Algorithms and Applications (available at: http://szeliski.org/Book) Python Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html) Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook) Online Python tutorials: https://docs.python.org/3/tutorial/ Other recommended books: - Kleinberg, Tardos. Algorithm Design. Addison Wesley. - Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.

Traditional frontal lectures

Please refer to the degree course regulations.

Exam: 1) Theory: 50% (written) 2) Practise: 50%, of which - 2/3 from assignments in Python, to be submitted by given deadlines during the course - 1/3 from a final project and presentation Theory: - written exam with open-ended questions - duration: 30 minutes Assignments: - The assignments and the final projects must be submitted in groups of size [ 3 – 5 ] Final project: - Algorithms, objectives and topics for the final project may be freely chosen - Ideas for projects and resources for it would be discussed in class

More references for Data Science: - Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press. (available at: http://infolab.stanford.edu/~ullman/mmdsn.html) For Deep Learning: - Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/) - Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/) Other recommended books: - Kleinberg, Tardos. Algorithm Design. Addison Wesley. - Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press. Book reference for Python: - Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html) Online tutorials for Python: - https://docs.python.org/3/tutorial/

Traditional frontal lectures

The course is an introduction to the basic toolkit of data analysis and machine learning using the Python programming language. Covered topics include: basics of digital image processing (8 hours); regression (6 hours); classification with discriminative and generative models (16 hours); optimization (8 hours); bias/variance (6 hours); regularization (5 hours); clustering (3 hours); dimensionality reduction (3 hours); introduction to neural networks (5 hours). For more information see the course website: https://sites.google.com/di.uniroma1.it/fds-2022-2023

Calculus and Linear Algebra, including taking derivatives, understanding matrix vector operations and notation Basic Probability and Statistics, including basics of probabilities, gaussian distributions, mean and standard deviation

Data Science: Bertsimas, O'Hair, Pulleyblank. The Analytics Edge. Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press (available at: http://infolab.stanford.edu/~ullman/mmdsn.html) Machine Learning: Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning Deisenroth, Faisal, Ong, 2020. Mathematics for Machine Learning (available at: https://mml-book.github.io/) Deep learning: Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/) Zhang Lipton Li Smola Book, 2019 Dive into Deep Learning (interactive book and code at: http://d2l.ai/index.html) Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/) Image Analysis and Recognition, Computer Vision: Richard Szeliski, 2010. Computer Vision: Algorithms and Applications (available at: http://szeliski.org/Book) Python Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html) Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook) Online Python tutorials: https://docs.python.org/3/tutorial/ Other recommended books: - Kleinberg, Tardos. Algorithm Design. Addison Wesley. - Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.

Please refer to the degree course regulations.

1) Theory: 50% (written) 2) Practise: 50%, of which - 2/3 from assignments in Python, to be submitted by given deadlines during the course - 1/3 from a final project and presentation Theory: - written exam with open-ended questions - duration: 30 minutes Assignments: - The assignments and the final projects must be submitted in groups of size [ 3 – 5 ] Final project: - Algorithms, objectives and topics for the final project may be freely chosen - Ideas for projects and resources for it would be discussed in class

Traditional frontal lectures

The course is an introduction to the basic toolkit of data analysis and machine learning using the Python programming language. Covered topics include: basics of digital image processing (8 hours); regression (6 hours); classification with discriminative and generative models (16 hours); optimization (8 hours); bias/variance (6 hours); regularization (5 hours); clustering (3 hours); dimensionality reduction (3 hours); introduction to neural networks (5 hours). For more information see the course website: https://sites.google.com/di.uniroma1.it/fds-2022-2023

Calculus and Linear Algebra, including taking derivatives, understanding matrix vector operations and notation Basic Probability and Statistics, including basics of probabilities, gaussian distributions, mean and standard deviation

Data Science: Bertsimas, O'Hair, Pulleyblank. The Analytics Edge. Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press (available at: http://infolab.stanford.edu/~ullman/mmdsn.html) Machine Learning: Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning Deisenroth, Faisal, Ong, 2020. Mathematics for Machine Learning (available at: https://mml-book.github.io/) Deep learning: Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/) Zhang Lipton Li Smola Book, 2019 Dive into Deep Learning (interactive book and code at: http://d2l.ai/index.html) Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/) Image Analysis and Recognition, Computer Vision: Richard Szeliski, 2010. Computer Vision: Algorithms and Applications (available at: http://szeliski.org/Book) Python Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html) Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook) Online Python tutorials: https://docs.python.org/3/tutorial/ Other recommended books: - Kleinberg, Tardos. Algorithm Design. Addison Wesley. - Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.

Please refer to the degree course regulations.

1) Theory: 50% (written) 2) Practise: 50%, of which - 2/3 from assignments in Python, to be submitted by given deadlines during the course - 1/3 from a final project and presentation Theory: - written exam with open-ended questions - duration: 30 minutes Assignments: - The assignments and the final projects must be submitted in groups of size [ 3 – 5 ] Final project: - Algorithms, objectives and topics for the final project may be freely chosen - Ideas for projects and resources for it would be discussed in class

Traditional frontal lectures