Course: 581SM - STATISTICAL MACHINE LEARNING 2018

Section outline

Select section Statistical Machine Learning

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 section Textbooks

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

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

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

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

Introduction
- Select activity Slides - Introduction
  
  Slides - Introduction File PDF
- Select activity Slides: recap of probability and inference
  
  Slides: recap of probability and inference File PDF
- Select activity Video - Introduction
  
  Video - Introduction URL
- Select activity Video: probability part I
  
  Video: probability part I URL
- Select activity Video: probability part 2
  
  Video: probability part 2 URL
- Select activity Video: estimation
  
  Video: estimation URL
Select section Lab - Data Science in Python: Pandas and Data Visualization

Lab - Data Science in Python: Pandas and Data Visualization
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 seaborne.
Before playing with Pandas, make sure you install Python 3. My suggestion is that you install the ANACONDA distribution - it has almost all the libraries needed already installed. You will need Jupyter Notebook. A good idea is to install also an IDE, like PyCharm CE.
- Select activity Anaconda distribution
  
  Anaconda distribution URL
- Select activity PyCharm IDE
  
  PyCharm IDE URL
- Select activity Notebooks and data
  
  Notebooks and data File ZIP
- Select activity Pandas exercise notebook
  
  Pandas exercise notebook File ZIP
Select section Bayesian regression and classification

Bayesian regression and classification
Introduction to Bayesian statistics, and Bayesian linear regression and classification.
- Select activity Slides: Bayesian Regression
  
  Slides: Bayesian Regression File PDF
- Select activity Video: Bayesian Regression
  
  Video: Bayesian Regression URL
- Select activity Slides: Bayesian Logistic Regression
  
  Slides: Bayesian Logistic Regression File PDF
- Select activity Video: Bayesian Logistic Classification (last year edition)
  
  Video: Bayesian Logistic Classification (last year edition) URL
- Select activity Video: Bayesian Logistic Classification and Random Functions
  
  Video: Bayesian Logistic Classification and Random Functions URL
Select section LAB: Scikit-Learn

LAB: Scikit-Learn
- Select activity Scikit-Learn Home Page
  
  Scikit-Learn Home Page URL
- Select activity Notebook on Scikit-Learn
  
  Notebook on Scikit-Learn File ZIP
Select section Gaussian Processes

Gaussian Processes
- Select activity Slides: Gaussian Processes
  
  Slides: Gaussian Processes File PDF
- Select activity GP Notebook
  
  GP Notebook File ZIP
Select section Variational Inference

Variational Inference
- Select activity Slides: Latent variables and EM
  
  Slides: Latent variables and EM File PDF
- Select activity Slides: classical variational inference
  
  Slides: classical variational inference File PDF
- Select activity Slides: advanced topics in variational inference
  
  Slides: advanced topics in variational inference File PDF
- Select activity Video: Latent variables and EM
  
  Video: Latent variables and EM URL
- Select activity Video: Variational Inference basics
  
  Video: Variational Inference basics URL
- Select activity Video: variational inference advanced topics
  
  Video: variational inference advanced topics URL
Select section Probabilistic Graphical Models

Probabilistic Graphical Models
- Select activity Video: Bayesian Networks and Markov Random Fields
  
  Video: Bayesian Networks and Markov Random Fields URL
- Select activity Video: Inference in Graphical Models
  
  Video: Inference in Graphical Models URL
- Select activity Video: Hidden Markov Models
  
  Video: Hidden Markov Models URL
- Select activity Probabilistic Programming repository
  
  Probabilistic Programming repository URL
- Select activity WebPPL
  
  WebPPL URL
Select section Deep Learning

Deep Learning
- 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 Video: Introduction to Deep Learning
  
  Video: Introduction to Deep Learning URL
- Select activity Video: SGD and regularization
  
  Video: SGD and regularization URL
- Select activity Video: dropout and Convolutional NN
  
  Video: dropout and Convolutional NN URL
- Select activity Lab: deep learning notebooks
  
  Lab: deep learning notebooks URL
- Select activity Video: Recurrent Nets, Autoencoders, Generative Models
  
  Video: Recurrent Nets, Autoencoders, Generative Models URL
- Select activity Instructions to login to Jupiter Notebook server
  
  Instructions to login to Jupiter Notebook server File PDF
- Select activity Material: Deep Learning Book, by Bengio&co
  
  Material: Deep Learning Book, by Bengio&co URL
Select section Final Exam

Final Exam
- Select activity Possible list of topics for presentation
  
  Possible list of topics for presentation Page
- Select activity Groups and group topics
  
  Groups and group topics Page
- Select activity Doc: group datasets
  
  Doc: group datasets URL