Subject: MACHINE LEARNING AND DEEP LEARNING (A.A. 2020/2021)
Unit Machine learning and deep learning
Related or Additional Studies (lesson)
The course will cover most of the techniques for pattern and data analysis. The main purpose will be offer a deep knowledge of the most important classification algorithms and model for automatically interpreting heterogeneous data. Learning techniques and methods will be covered as well in conjunction with basic supervised and unsupervised learning theory and methods.
Basic knowledge of statistics and linear algebra
Classification theory, taxonomy and metrics:
Introduction to Bayesian Probability:
Linear Models for Classification:
Margin methods and SVM
-Decision Trees and Random Forest
Unsupervised Learning and Dimensionality Reduction
-PCA and LLE
Neural Networks Introduction:
-Perceptron and MLE
-RNN and sequential data processing
-Generative NN VAE
Generative NN GAN
-Function approximation and RL
-Deep reinforcement Learning
Slide from the lecturer. Lab session coding all the presented techniques in Python and Pytorch.
Oral exam with both thory and math derivations. 3 Questions: one purely about classification theory one about the classifier and its math derivation one about the comparison of different techniques in solving a real life problem
Knowledge and Understanding:
Know and understand the key techniques of pattern recognition and machine learning for data analysis of heterogeneous nature .
Applying knowledge and understanding:
Know how to apply the main classification algorithms of data, temporal sequences of information and complex patterns such as images and know how to apply the main techniques of machine learning is of type supervised that unsupervised .
Autonomy of Findings
independent judgment in analyzing and designing complex systems , assessing the impact of information technology solutions in the application context , both in relation to the technical aspects organizational aspects and proving to actively participate in decision-making in interdisciplinary contexts .
describe a heterogeneous stakeholders in a clear and understandable information , ideas , problems and solutions as well as technical aspects ;
-the capacity to recognize the need for independent learning throughout the lifespan , given the high rate of technological and methodological innovation in the area of Computer Engineering ;
- Ability to independently acquire new expertise from the literature
The course will use the following textbooks freely available on the web
[ISL]: An Introduction to Statistical Learning. James, Witten, Hastie and Tibshirani.
[ESL]: The Elements of Statistical Learning, Second Edition. Hastie, Tibshirani and
[DLB]: Deep Learning. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Suggested further readings:
[RL]: Reinforcement Learning Sutton, Barto.
[PRML]:Pattern Recognition and Machine Learning. Bishop Christopher. Readings are intended to be completed after classes.