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Machine Learning - A.Y. 2017/2018

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 14.00-16.00 aula 1 castro laurenziano
Wednesday 16:30-18:30 aula 1 castro laurenziano

Important Notes

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: underfitting, overfitting, model selection, error analysis.

Laboratory

*Students will experiment the main algorithms using the Weka ML software Weka or scikit-learn

Textbooks

Additional useful texts:

Resources:

Exam

  • Written exam on course material (60% of final grade)
  • 6-8 homeworks using the Weka ML toolkit
  • Weka or scikit-learn project (40%)
  • rounding on final grade: +0.9 (max!!) depending on handled homeworks and presence in class
  • 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

Project 2016 (Spring): GAME WINNER PREDICTION CONTEST

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

To be decided

How a project is evaluated:

  • Simple problem, easy-to-model easy-to-describe instances, small dataset, standard ML learning algorithms using WEKA: up to 25/30
  • 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 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

MANDATORY!!

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

Slides and course materials (those with date=2017 are UPDATED)

Timetable Topic PPT Details
2017 Introduction   ML201718Introduction.pdf
2017 Inductive Classification 2.Classification.pptx 2.Classification.pptx
2016 Introduction to WEKA toolkit 1 ppt pdf
2016 Introduction to WEKA toolkit 2 ppt pdf
2017 Decision Trees 3.dtrees.ppt 3.dtrees.pdf
2016 Performance Evaluation: error estimates, confidence intervals, one/two-tail test   pdf
2017 Neural Networks 5.neural.pptx 5.neural.pdf
2017 Deep Learning (Convolutional NN and denoising autoencoders) 5b.Deeplearning.pptx 5b.Deeplearning.pdf
2016 Ensemble methods (bagging, boosting) ppt pdf
2016 Support Vector Machines pptx pdf
2016 Probabilistic learning: Maximum Likelyhood Learning, Naive Bayes ppt pdf
201x Clustering    
2016 Unsupervised learning: Association Rules ppt pdf
2016 Unsupervised Learning: Reinforcement Learning and Q-Learning ppt pdf
2016 Unsupervised Learning: genetic Algorithms ppt pdf

Syllabus

  • Concept Learning and the General-to-Specific Ordering
  • Decision Tree Learning
  • Evaluation methods: experimental and theoretical methods
  • Ensamble methods
  • Artificial Neural Networks and Deep learning
  • Support Vector Machines
  • Clustering
  • Maximum Likelyhood Learning
  • Data Mining: Apriori algorithm and frequent itemset mining
  • Reinforcement learning and Q-Learning
  • Genetic Algorithms
Topic attachments
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