Unsupervised machine learning projects Hugo Gabriel Eyherabide - PowerPoint PPT Presentation

Jan 23, 2024 •48 likes •88 views

Unsupervised machine learning projects Hugo Gabriel Eyherabide (hugo.eyherabide@helsinki.fi) 3 projects involving all methods seen in UML First project: PCA and FA Second project: Whitening and ICA Third project: Clustering and Nonlinear

Unsupervised machine learning projects Hugo Gabriel Eyherabide (hugo.eyherabide@helsinki.fi) 3 projects involving all methods seen in UML First project: PCA and FA Second project: Whitening and ICA Third project: Clustering and Nonlinear dimensional reduction Submissions consist in: 1) Codes with implementations 2) Executable script with calculations and figures 3) Written report Pass => >50 points in each project. Progress counts! Delay => Penalty of 10 points per day.
First project: Principal component analysis and factor analysis 1) Basics of PCA 2) Basics of FA 3) Application to compression and denoising
Second project: Whitening and ICA 1) Basics of ICA 2) ICA using Kurtosis 3) ICA using Maximum Likelihood 4) Application to natural image separation
Third project: Clustering and Nonlinear dimensional reduction 1) K means and Gaussian mixtures 2) Self-organizing maps 3) Isomap, Kernel-PCA, Laplacian eigenmaps, neighbourhoods

Recommend

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

398 views • 26 slides

Unsupervised Learning Andrea Passerini passerini@disi.unitn.it Machine Learning Unsupervised

Unsupervised Learning Andrea Passerini passerini@disi.unitn.it Machine Learning Unsupervised Learning Unsupervised Learning Setting Supervised learning requires the availability of labelled examples Labelling examples can be an extremely

752 views • 27 slides

Machine Learning for NLP Unsupervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain

Machine Learning for NLP Unsupervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain Sciences University of Trento 1 Unsupervised learning In unsupervised learning, we learn without training data. The idea is to find a

998 views • 67 slides

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised learning: X - y pairs, f(x) function approximation Unsupervised learning: only X, no y Exploring the space of X measurements,

440 views • 14 slides

Detecting and Characterizing Events Unsupervised Machine Learning for Social Science Allison

Detecting and Characterizing Events Unsupervised Machine Learning for Social Science Allison J.B. Chaney Princeton University supervised learning unsupervised learning methods input output prediction exploration goals 9 3 1 ? 5 6

920 views • 59 slides

Machine Learning Lecture 6 Unsupervised Learning with a bit of Supervised Learning Clustering

Machine Learning Lecture 6 Unsupervised Learning with a bit of Supervised Learning Clustering and nearest neighbour classifiers. Justin Pearson 1 2020 1 http://user.it.uu.se/~justin/Teaching/MachineLearning/index.html 1 / 27 Supervised

813 views • 27 slides

A Gentle Introduction to Machine Learning Supervised learning, unsupervised learning (very

9/23/2020 Outline of Machine Learning Lectures Introduction to machine learning (two lectures) A Gentle Introduction to Machine Learning Supervised learning, unsupervised learning (very brief) First Lecture Reinforcement learning

358 views • 13 slides

Introduction to PCA Unsupervised Learning in R Unsupervised learning Two methods of

UNSUPERVISED LEARNING IN R Introduction to PCA Unsupervised Learning in R Unsupervised learning Two methods of clustering finding groups of homogeneous items Next up, dimensionality reduction Find structure in features

904 views • 29 slides

Machine Learning Supervised Learning Unsupervised Learning CSE 446: Expectation Maximization

Machine Learning Supervised Learning Unsupervised Learning CSE 446: Expectation Maximization Reinforcement Learning (EM) Parametric Non-parametric Winter 2012 Fri K means & Agglomerative Clustering Daniel Weld Mon Expectation

329 views • 10 slides

Machine Learning and Data Mining Clustering (adapted from) Prof. Alexander Ihler Unsupervised

+ Machine Learning and Data Mining Clustering (adapted from) Prof. Alexander Ihler Unsupervised learning Supervised learning Predict target value (y) given features (x) Unsupervised learning Understand patterns of

559 views • 27 slides

Practical Unsupervised Learning INFO/CS 4300, Spring 2016 Jack Hessel Unsupervised Learning is

Practical Unsupervised Learning INFO/CS 4300, Spring 2016 Jack Hessel Unsupervised Learning is Cool! But how can we use this in our projects? 1. 2. But first, lets look at our dataset... Data Dimensionality! Probability of Lung Cancer

999 views • 55 slides

MACHINE-LEARNING IN CHEMISTRY Yashasvi S. Ranawat Filippo Federici UNSUPERVISED LEARNING

MACHINE-LEARNING IN CHEMISTRY Yashasvi S. Ranawat Filippo Federici UNSUPERVISED LEARNING finds similarities in complex data records does not require knowledge of properties/outputs, only descriptors/inputs sensitive to the

624 views • 9 slides

Unsupervised Learning II George Konidaris gdk@cs.brown.edu Fall 2019 Machine Learning Subfield

Unsupervised Learning II George Konidaris gdk@cs.brown.edu Fall 2019 Machine Learning Subfield of AI concerned with learning from data . Broadly, using: Experience To Improve Performance On Some Task (Tom Mitchell, 1997)

437 views • 33 slides

Unsupervised Learning George Konidaris gdk@cs.brown.edu Fall 2019 Machine Learning Subfield of

Unsupervised Learning George Konidaris gdk@cs.brown.edu Fall 2019 Machine Learning Subfield of AI concerned with learning from data . Broadly, using: Experience To Improve Performance On Some Task (Tom Mitchell, 1997)

888 views • 45 slides

MACHINE LEARNING Overview 1 MACHINE LEARNING Oral Presentations of Projects Start at 9h15 am

MACHINE LEARNING MACHINE LEARNING Overview 1 MACHINE LEARNING Oral Presentations of Projects Start at 9h15 am and last until 12h30 2 MACHINE LEARNING Exam Format The exam lasts a total of 40 minutes: -Upon entering the room, you pick at

919 views • 57 slides

Unsupervised Learning Shan-Hung Wu shwu@cs.nthu.edu.tw Department of Computer Science, National

Unsupervised Learning Shan-Hung Wu shwu@cs.nthu.edu.tw Department of Computer Science, National Tsing Hua University, Taiwan Machine Learning Shan-Hung Wu (CS, NTHU) Unsupervised Learning Machine Learning 1 / 49 Outline Unsupervised

1.14k views • 93 slides

Unsupervised Learning Shan-Hung Wu shwu@cs.nthu.edu.tw Department of Computer Science, National

2.09k views • 193 slides

Deep Learning: Intro Juhan Nam Review of Traditional Machine Learning The traditional machine

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Deep Learning: Intro Juhan Nam Review of Traditional Machine Learning The traditional machine learning pipeline Temporal Frame-level Unsupervised Classifier Summary

613 views • 27 slides

Machine Learning 2 DS 4420 - Spring 2020 Clustering I Byron C. Wallace Unsupervised learning

Machine Learning 2 DS 4420 - Spring 2020 Clustering I Byron C. Wallace Unsupervised learning So far we have reviewed some fundamentals, discussed Maximum Likelihood Estimation (MLE) for probabilistic models, and neural networks/backprop

931 views • 75 slides

Softmax Classifier + SGD Todays Class Intro to Machine Learning What is Machine Learning?

CS6501: Deep Learning for Visual Recognition Softmax Classifier + SGD Todays Class Intro to Machine Learning What is Machine Learning? Supervised Learning: Classification with K-nearest neighbors Unsupervised Learning: Clustering with

1.36k views • 91 slides

Machine Learning for NLP Readings in unsupervised Learning Aurlie Herbelot 2018 Centre for

Machine Learning for NLP Readings in unsupervised Learning Aurlie Herbelot 2018 Centre for Mind/Brain Sciences University of Trento 1 Hashing 2 Hashing: definition Hashing is the process of converting data of arbitrary size into

845 views • 48 slides

Machine Learning Fall 2017 Unsupervised Learning (Clustering: k -means, EM, mixture models)

Machine Learning Fall 2017 Unsupervised Learning (Clustering: k -means, EM, mixture models) Professor Liang Huang (Chaps. 15-16 of CIML) Roadmap CIML Chaps. 3, 4,5,7,11,17 ,18 so far: (large-margin) supervised learning online learning:

815 views • 54 slides

INFO 1998: Introduction to Machine Learning Lecture 9: Clustering and Unsupervised Learning INFO

INFO 1998: Introduction to Machine Learning Lecture 9: Clustering and Unsupervised Learning INFO 1998: Introduction to Machine Learning If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the

410 views • 27 slides

COMP24111: Machine Learning and Optimisation Chapter 1: Machine Learning Basics Dr. Tingting Mu

COMP24111: Machine Learning and Optimisation Chapter 1: Machine Learning Basics Dr. Tingting Mu Email: tingting.mu@manchester.ac.uk Outline We are going to learn the following concepts: Machine learning. Unsupervised, supervised,

293 views • 26 slides