Course: 581SM - STATISTICAL MACHINE LEARNING 2019

Section outline

Select section Statistical Machine Learning

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Statistical Machine Learning

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This is the moodle page of the Statistical Machine Learning of the Master Program in Data Science and Scientific Computing. Here you will find the material of the course, including slides, videos, textbooks, the lab assignments, and the instructions for the final project.
Lectures.
Wednesday 09.30-13.00
Thursday 11.00-13.00 and 14.00-16.00
- Select activity Annunci
  
  Annunci Forum
- Select activity News
  
  News Forum
- Select activity Discussion Forum
  
  Discussion Forum
- Select activity Questions on theory and exercises
  
  Questions on theory and exercises Forum
  
  Please write here your questions on theory and exercises, so that everybody can access the answers. Only personal questions will be answered by email.
Select section Textbooks

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Textbooks
Textbooks containing the material treated in the course (and much more). In pdf, for your private use. Do not distribute.
- Select activity Bishop - Pattern Recognition and Machine Learning
  
  Bishop - Pattern Recognition and Machine Learning File PDF
- Select activity Barber - Bayesian reasoning and Machine Learning
  
  Barber - Bayesian reasoning and Machine Learning File PDF
- Select activity Rasmussen Williams - Gaussian Processes for Machine Learning
  
  Rasmussen Williams - Gaussian Processes for Machine Learning File PDF
- Select activity K. P. Murphy - Machine learning: a probabilistic perspective
  
  K. P. Murphy - Machine learning: a probabilistic perspective File PDF
Select section Links and papers

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Links and papers
Here you can find useful links and other material to read.
- Select activity UCI Machine Learning Repo
  
  UCI Machine Learning Repo URL
- Select activity Kaggle datasets
  
  Kaggle datasets URL
Select section Datasets for Regression

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Datasets for Regression
Here you can find several datasets to experiment with.
- Select activity Synthetic Regression Data
  
  Synthetic Regression Data File ZIP
- Select activity Geographical localisation of music
  
  Geographical localisation of music File ZIP
- Select activity Wine Quality
  
  Wine Quality File ZIP
- Select activity Energy efficiency of buildings
  
  Energy efficiency of buildings File ZIP
- Select activity Combined Cycle Power Plant
  
  Combined Cycle Power Plant URL
- Select activity Yacht Hydrodynamics
  
  Yacht Hydrodynamics URL
- Select activity Concrete Compressive Strength
  
  Concrete Compressive Strength URL
- Select activity Household Power Consumption
  
  Household Power Consumption URL
- Select activity Year of release of songs
  
  Year of release of songs URL
- Select activity Naval Propulsion Maintenance
  
  Naval Propulsion Maintenance URL
- Select activity Istanbul Stock Exchange
  
  Istanbul Stock Exchange URL
Select section Datasets for Classification

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Datasets for Classification
Here you can find several datasets to experiment with classification techniques.
- Select activity Synthetic Classification Data
  
  Synthetic Classification Data File ZIP
- Select activity Default of credit cards
  
  Default of credit cards File ZIP
- Select activity Online news popularity
  
  Online news popularity File ZIP
- Select activity Wine Quality
  
  Wine Quality File ZIP
- Select activity Geographical localisation of music
  
  Geographical localisation of music File ZIP
- Select activity Istanbul Stock Exchange
  
  Istanbul Stock Exchange URL
- Select activity Banknote authentication
  
  Banknote authentication URL
- Select activity Predicting Census
  
  Predicting Census URL
- Select activity Malicious Software
  
  Malicious Software URL
- Select activity MNIST dataset
  
  MNIST dataset URL
- Select activity Not-MNIST datasets (alphabet letters)
  
  Not-MNIST datasets (alphabet letters) URL
- Select activity SVHN (Street View house number)
  
  SVHN (Street View house number) URL
Select section Introduction

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Introduction
- Select activity Video - Introduction
  
  Video - Introduction URL
- Select activity Slides - Introduction
  
  Slides - Introduction File PDF
Select section Introduction to Probability

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Introduction to Probability
- Select activity Link to recorded lectures
  
  Link to recorded lectures Page
- Select activity Notes: introduction to probabilities
  
  Notes: introduction to probabilities File PDF
- Select activity Blackboard: introduction to probabilities
  
  Blackboard: introduction to probabilities File PDF
- Select activity Exercises: introduction to probabilities
  
  Exercises: introduction to probabilities URL
- Select activity Exercise notebook: introduction to probability
  
  Exercise notebook: introduction to probability File ZIP
- Select activity Exercise sheet on probabilities
  
  Exercise sheet on probabilities File PDF
- Select activity Exercises on probability: submission
  
  Exercises on probability: submission Assignment
- Select activity Blackboard: question-time session
  
  Blackboard: question-time session File PDF
- Select activity Homework 01 solutions
  
  Homework 01 solutions File PDF
Select section Probabilistic Graphical Models

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Probabilistic Graphical Models
- Select activity Recorded Lectures: Probabilistic Graphical Models
  
  Recorded Lectures: Probabilistic Graphical Models Page
- Select activity Recorded Lectures: Inference in PGM
  
  Recorded Lectures: Inference in PGM Page
- Select activity Blackboard: Bayesian Networks
  
  Blackboard: Bayesian Networks File PDF
- Select activity Blackboard: Markov Random Fields
  
  Blackboard: Markov Random Fields File PDF
- Select activity Blackboard: Inference in PGM
  
  Blackboard: Inference in PGM File PDF
- Select activity Slides: PGM
  
  Slides: PGM File PDF
- Select activity Video: notebook on probabilistic programming
  
  Video: notebook on probabilistic programming URL
- Select activity Notebook: probabilistic programming
  
  Notebook: probabilistic programming File IPYNB
- Select activity Homework: probabilistic programming
  
  Homework: probabilistic programming File PDF
- Select activity Probabilistic Programming repository
  
  Probabilistic Programming repository URL
- Select activity Exercise sheet 2. Submission by April 1st 10:30 am
  
  Exercise sheet 2. Submission by April 1st 10:30 am Assignment
- Select activity Homework 02 solutions
  
  Homework 02 solutions File PDF
- Select activity Video: notebook on PGM
  
  Video: notebook on PGM URL
- Select activity Notebook: PGM
  
  Notebook: PGM File ZIP
- Select activity Exercise sheet: PGM
  
  Exercise sheet: PGM File PDF
- Select activity Exercise sheet 3: PGM. Submission by April 14.
  
  Exercise sheet 3: PGM. Submission by April 14. Assignment
- Select activity Homework 03 solutions
  
  Homework 03 solutions File PDF
Select section Markov Chain Monte Carlo

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Markov Chain Monte Carlo
- Select activity Recorded Lectures: MCMC
  
  Recorded Lectures: MCMC Page
- Select activity Blackboard: MCMC
  
  Blackboard: MCMC File PDF
- Select activity Slides: MCMC
  
  Slides: MCMC File PDF
- Select activity Book: Gelman et al. - Bayesian Data Analysis, third edition
  
  Book: Gelman et al. - Bayesian Data Analysis, third edition File PDF
- Select activity Reading: Hamiltonian MC and no-U-turn sampling
  
  Reading: Hamiltonian MC and no-U-turn sampling File PDF
- Select activity Lab Video: mcmc
  
  Lab Video: mcmc URL
- Select activity Python notebook: mcmc
  
  Python notebook: mcmc File ZIP
- Select activity Exercise sheet: mcmc
  
  Exercise sheet: mcmc File PDF
- Select activity Exercise sheet mcmc: submission by 21/04
  
  Exercise sheet mcmc: submission by 21/04 Assignment
- Select activity Homework 04 solutions
  
  Homework 04 solutions File PDF
Select section Hidden Markov Models

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Hidden Markov Models
- Select activity Recorded Lectures
  
  Recorded Lectures Page
- Select activity Blackboard: HMM
  
  Blackboard: HMM File PDF
- Select activity Dow Jones Dataset
  
  Dow Jones Dataset File ZIP
- Select activity ECG dataset
  
  ECG dataset File ZIP
Select section Bayesian regression and classification

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Bayesian regression and classification
Introduction to Bayesian statistics, and Bayesian linear regression and classification.
- Select activity Recorded Lectures: Bayesian linear regression
  
  Recorded Lectures: Bayesian linear regression Page
- Select activity Blackboard: Gaussian Distributions
  
  Blackboard: Gaussian Distributions File PDF
- Select activity Blackboard: Bayesian Linear Regression
  
  Blackboard: Bayesian Linear Regression File PDF
- Select activity Notes: linear regression
  
  Notes: linear regression File PDF
- Select activity Slides: bayesian linear regression
  
  Slides: bayesian linear regression File PDF
- Select activity Recorded lectures: bayesian linear classification
  
  Recorded lectures: bayesian linear classification Page
- Select activity Notes: Bayesian Linear Classification
  
  Notes: Bayesian Linear Classification File PDF
- Select activity Blackboard: Bayesian Linear Classification
  
  Blackboard: Bayesian Linear Classification File PDF
- Select activity Large scale kernel approximation: slides + references
  
  Large scale kernel approximation: slides + references File ZIP
- Select activity Large scale kernel approximation: github
  
  Large scale kernel approximation: github URL
Select section Lab - Data Science in Python: Pandas, Data Visualization, Scikit-learn

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Lab - Data Science in Python: Pandas, Data Visualization, Scikit-learn
Pandas is the main library to manipulate and explore datasets in Python. Here you will find Jupyter Notebooks tutorials and links to tutorials to learn Pandas. We will also cover some fundamentals of data visualization in matplotlib and seaborn. The last notebook is about scikit-learn, a coprehensive machine learning in Python.
Within the notebooks, you will also find some exercises. These are not to be submitted, but can help you learning.
I assume you have Python 3 up and running.
- Select activity Notebooks and data
  
  Notebooks and data File ZIP
- Select activity Pandas home page
  
  Pandas home page URL
- Select activity Matplotlib Home Page
  
  Matplotlib Home Page URL
- Select activity Seaborn Home Page
  
  Seaborn Home Page URL
- Select activity Scikit-Learn Home Page
  
  Scikit-Learn Home Page URL
Select section Gaussian Processes

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Gaussian Processes
- Select activity Recorded Lectures: GP
  
  Recorded Lectures: GP Page
- Select activity Blackboard: GP
  
  Blackboard: GP File PDF
- Select activity Notes: Gaussian Processes
  
  Notes: Gaussian Processes File PDF
- Select activity Slides: CO2 example
  
  Slides: CO2 example File PDF
- Select activity Python Notebook: GP
  
  Python Notebook: GP File ZIP
- Select activity Exercise sheet: GP
  
  Exercise sheet: GP File ZIP
- Select activity Exercise sheet: GP. Submission by May 10, 11:00
  
  Exercise sheet: GP. Submission by May 10, 11:00 Assignment
Select section Variational Inference

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Variational Inference
- Select activity Video: Latent variables and EM
  
  Video: Latent variables and EM URL
- Select activity Slides: Latent variables and EM
  
  Slides: Latent variables and EM File PDF
- Select activity Video: Variational Inference basics
  
  Video: Variational Inference basics URL
- Select activity Slides: classical variational inference
  
  Slides: classical variational inference File PDF
- Select activity Video: variational inference advanced topics
  
  Video: variational inference advanced topics URL
- Select activity Slides: advanced topics in variational inference
  
  Slides: advanced topics in variational inference File PDF
- Select activity Reading: An introduction to variational methods for graphical models
  
  Reading: An introduction to variational methods for graphical models File PDF
- Select activity Reading: Black Box Variational Inference
  
  Reading: Black Box Variational Inference File PDF
- Select activity Lab
  
  Lab
- Select activity Video: Variational Inference Lab
  
  Video: Variational Inference Lab URL
- Select activity Notebooks: Variational Inference
  
  Notebooks: Variational Inference File ZIP
- Select activity Exercise sheet
  
  Exercise sheet File PDF
- Select activity Exercise sheet VI: submission by May 22
  
  Exercise sheet VI: submission by May 22 Assignment
- Select activity Homework 06 solutions
  
  Homework 06 solutions File PDF
Select section Deep Learning

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Deep Learning
- Select activity Recorded Lectures: deep learning
  
  Recorded Lectures: deep learning Page
- Select activity Blackboard: Deep Learning
  
  Blackboard: Deep Learning File PDF
- Select activity Slides: Introduction to Deep Learning
  
  Slides: Introduction to Deep Learning File PDF
- Select activity Slides: SGD and regularization
  
  Slides: SGD and regularization File PDF
- Select activity Slides: Convolutional and Recurrent Nets
  
  Slides: Convolutional and Recurrent Nets File PDF
- Select activity Slides: Autoencoders and generative models
  
  Slides: Autoencoders and generative models File PDF
- Select activity Material: Deep Learning Book, by Bengio&co
  
  Material: Deep Learning Book, by Bengio&co URL
- Select activity Deep Learning Lab
  
  Deep Learning Lab
- Select activity PyTorch Lab: presentation
  
  PyTorch Lab: presentation Page
- Select activity Notebooks: github repo
  
  Notebooks: github repo URL
Select section Exercise classes

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Exercise classes
- Select activity git repository
  
  git repository URL
Select section Final Exam

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Final Exam
- Select activity When you plan to take the exam?
  
  When you plan to take the exam? Choice
- Select activity Preferenze Gruppi
  
  Preferenze Gruppi Wiki
- Select activity Groups
  
  Groups Wiki