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Machine Learning - A.Y. 2018/2019

Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. Machine learning algorithms can be applied to virtually any scientific and non-scientific field (health, security and cyber-security, management, finance, automation, robotics, marketing..).

Instructor Telephone Office hours Studio
Paola Velardi 06-49918356 send e-mail Via Salaria 113 - 3° floor n. 3412

Course schedule

FIRST semester:

When   Where
Monday 14.00-16.30 aula 1 castro laurenziano
Thursday 14:00-16:30 aula 1 castro laurenziano

Important Notes

IMPORTANT: The course will start on Thursday 27th.

The course is taught in English. Attending classes is HIGHLY recommended (homeworks, mid-term, laboratory)

Homeworks and self-assessment tests are distributed via the google group, you MUST register

Summary of Course Topics

The course introduces motivations, paradigms and applications of machine learning. It is to be considered an introductory course.

Topics Supervised learning: decision trees, instance-based learning, naīve Bayes, support vector machine, neural networks, deep learning, ensamble methods. Unsupervised learning: clustering, association rules. Semi-supervised learning. Reinforcement learning. Genetic algorithms and genetic programming. Issues in machine learning: feature engineering, model selection, error analysis.


In-class labs (bring your computer on Lab days!) are dedicated to learning the design of practical machine learning systems: feature engineering, model selection, error analysis. We will use mostly the scikit-learn library, and Tensor Flow

Students with insufficient programming background can also use Weka.

Lab material will be provided before lab days via the Google group. Lab assistant is Dr. Stefano Faralli.


There are plenty of on-line books and resources on Machine Learning. We list here some of the most widely used textbooks:

Additional useful texts:


A dataset search engine: https://toolbox.google.com/datasetsearch


  • Written exam on course material (60% of final grade)
  • Scikit-learn project (40%)
  • rounding on final grade: +0.5 (max!!)
  • Examples of mid-term pdf pdf

Project 2016 (Fall): Predicting forest cover type

The project is described here

The data set can be downloaded from DRIVE https://drive.google.com/file/d/0By8kOQZC1_qZTXJLdGx0ZkdiX2c/view?usp=sharing


The spring 2016 project was be a competion among student teams (max 3 students per team). The task is to predict the winner of a Role Playing Game (RPG) with direct clash. Students will be given a large dataset with detailed information on thousands of games, including the ID of the two competitors, the date of the match and the winner ID. The students will deliver the Predictor by the end of June (according to precise project specifications). Instructors will feed the systems with the details of additional games (not in the learning set) and compute the precision of each system at predicting the winner ID.

The project description is found here The learning dataset coun be downloaded here

Project 2017-18


How a project is evaluated:

  • Simple problem, easy-to-model easy-to-describe instances, small dataset, standard ML learning algorithms using WEKA: up to 25/26
  • Simple problem, easy-to-model easy-to-describe instances, medium-large datset, some complex algorithm not in weka, use of sckit-learn or a more efficient implementation of existing algorithm (e.g. some ad-hoc software developed): up to 28
  • Original problem, complex dataset, torough data analysis and feature/parameter fitting, not straightforward use of algorithms or new algorithm or ad-hoc implementation: up to 30 L

Three very good projects: Deep-Reinforcement-Learning-Proyect-Documentation-Alfonso-Oriola.pdf, A Framework for Genetic Algorithms, RainForestML2016Pantea.pdf

NOTE: Please read carefully how a project is evaluated, and read the two project examples above (they have been both rated 30L). Once a project is delivered and evaluated, the students cannot complain that the the grade is too low. We are here providing clear indications of what is expected to get the maximum grade. We also expect original work: plagiarism will be punished.

Google Group


Please Subscribe to Machine Learning 2018 Group Machine Learning 2018-19 on Google Groups

Slides and course materials (download only those with date=2018)

Timetable Topic PPT PDF Suggested readings
2018 Introduction to ML. Course syllabus and course organization.   ML2018Introduction.pdf  
2018 Building ML systems 2.BuildingMachineLearningSystems.pptx 2.BuildingMachineLearningSystems.pdf https://ai.stanford.edu/~nilsson/MLBOOK.pdf (Chapter 1)
2018 Classifiers: Decision Trees 3.dtrees.ppt 3.dtrees.pdf

Decision Trees: http://www.cs.princeton.edu/courses/archive/spr07/cos424/papers/mitchell-dectrees.pdf

Random Forests: http://www.math.mcgill.ca/yyang/resources/doc/randomforest.pdf

2018 Practical ML: feature engineering 4.Feature_Engineering.pptx 4.Feature_Engineering.pdf http://www.machinelearningtutorial.net/2017/06/17/feature-engineering-in-machine-learning/
2017 Performance Evaluation: error estimates, confidence intervals, one/two-tail test   4.evaluation.pdf, chapter5-ml-EVALUATION.pdf
2018 Neural Networks 5.neural.pptx  



2017 Deep Learning (Convolutional NN and denoising autoencoders)   5b.Deeplearning.pdf see pointers in slides
2017 Ensemble methods (bagging, boosting) ppt pdf  
2017 Support Vector Machines pptx pdf SVM.pdf
2017 Probabilistic learning: Maximum Likelyhood Learning, Naive Bayes ppt pdf  
201x Clustering    

Note: community detection (aka of clustering) is presented

in Web and Social Information Extraction during 2nd semester

2017 Unsupervised learning: Association Rules ppt pdf  
2017 Unsupervised Learning: Reinforcement Learning and Q-Learning 10.reinforcmentQ.pptx 10.reinforcmentQ.pdf https://github.com/junhyukoh/deep-reinforcement-learning-papers#all-papers
2016 Unsupervised Learning: genetic Algorithms ppt pdf  

Syllabus (2018-19)

  • What is machine learning. Types of learning. Workflow of ML systems.
  • Classifiers. Decision Tree Learning. Random Forest
  • Feature engineering
  • Evaluation: performance measures, confidence intervals and hypothesis testing
  • Ensamble methods
  • Artificial Neural Networks
  • Deep learning (Convolutional networks, Denoising Autoencoders)
  • Support Vector Machines
  • Maximum Likelyhood Learning and Naive Bayes
  • Unsupervised Rule learning: Apriori algorithm and frequent itemset mining
  • Reinforcement learning and Q-Learning, Deep Q
  • Tools: Weka, Scikit-learn, Tensor flow
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Topic revision: r250 - 2018-10-16 - PaolaVelardi

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