Outline Large margin classifiers Primal/Dual formulation of the SVM Kernel SVMs Hard-margin / soft-margin SVM and Hinge Loss SVMs beyond classification Applications 1/25 Linear Classifiers with Margin Let φ(x) be some feature map mapping the data in Rd to some feature space RD. We consider functions […]
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Exercises for the course Machine Learning 1 Winter semester 2020/21 Abteilung Maschinelles Lernen Institut fu ̈r Softwaretechnik und theoretische Informatik Fakult ̈at IV, Technische Universit ̈at Berlin Prof. Dr. Klaus-Robert Mu ̈ller Email: klaus-robert.mueller@tu-berlin.de Exercise Sheet 8 Exercise 1: Boosted Classifiers (15 + 15 P) We consider a two-dimensional dataset x1, . . . ,
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Wojciech Samek & Gr¨goire Montavon Prototypes vs LDA ML1 Lecture 4: Fisher Linear Discriminant 2 Nearest Centroid Classifier ML1 Lecture 4: Fisher Linear Discriminant 3 Prototypes: Psychological Models of Abstract Ideas ML1 Lecture 4: Fisher Linear Discriminant 4 Prototypes: Psychological Models of Abstract Ideas ML1 Lecture 4: Fisher Linear Discriminant 5 Prototypes: Psychological Models of
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Machine Learning 1 TU Berlin, WiSe 2020/21 Decision Trees, Random Forests, Boosting (40 P) The goal of this homework is to extend decision trees, using (1) random forests or (2) boosting. For this, we will make use of an existing decision tree implementation (available in scikit-learn), that we can then reuse for implementing the two
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lecture/12-em-annotated.pdf lecture/13-poe-annotated.pdf lecture/14-xai-annotated.pdf lecture/lecture1-annotated.pdf lecture/lecture10.pdf lecture/lecture11.pdf 1/24 Outline � Latent Variable Models � The Expectation Maximization Procedure � Gaussian Mixture Models � K-Means Clustering � Kernel K-Means 2/24 Motivation PCA of Iris dataset PCA of Boston dataset PCA of Diabetes dataset PCA of Digits dataset Complex data cannot be modeled accurately by standard probability distributions
Family name Student ID First name Signature of candidate University of Toronto Electrical and Computer Engineering PA 1 25 2 14 3 12 4 14 5 25- 6 16 7 32 8- 9- 10 – 11 – 12 L 138 Completed by examiner: Left exam room Early submission Special notes: from…………. at………….. until …………. NI
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Lesson 06 – Thread-Level Parallelism: Introduction Introduction Introduction Pipelining became universal technique in 1985 Overlaps execution of instructions Beyond pipelining, Instruction Level Parallelism (ILP) Executes instructions in parallel There are two main approaches: Hardware-based dynamic approaches: Software-based static approaches: Used in server and Not as successful outside of desktop processors
Wojciech Samek & Grégoire Montavon About myself 1. Interpretability & Explainability 2. Neural Network Compression 3. Federated Learning 4. Applications of Deep Learning ML1 Lecture 3: Dimensionality Reduction and Principle Component Analysis 2 This Lecture 1. Dimensionality reduction 2. PrincipleComponentAnalysis 1. What are Principle Components? 2. How to find/calculate them 1. Lagrange Multipliers 3. What
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Machine Learning 1 TU Berlin, WiSe 2020/21 In [1]: import numpy,sklearn,sklearn.datasets,utils %matplotlib inline Principal Component Analysis In this exercise, we will experiment with two different techniques to compute the PCA components of a dataset: • Standard PCA: The standard technique based on eigenvalue decomposition. • Iterative PCA: A technique that iteratively optimizes the PCA objective.
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GOSSIPING Distributed Systems (Hans‐Arno Jacobsen) 1 Pixabay.com Gossiping in Distributed Systems • Endless process of randomly choosing two nodes and have them exchange information Seminal paper form 1987 • I.e., repeated probabilistic exchange of information between two nodes • Information spreads within group of nodes • A.k.a. epidemic algorithms where a disease spreads or infects
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