Introduction to Analysis of Algorithms Final Exam CS4820/5820 Fall 2021 This exam consists of 6 questions on a total of 10 pages, worth a total of 70 points. There is an extra (empty) page included at the end. Please provide the answer at the space right under the question. You may also use the back […]
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Machine Learning Lecture 9: SVM and Kernels Prof. Dr. ̈nnemann Data Analytics and Machine Learning Group Technical University of Munich Copyright By PowCoder代写 加微信 powcoder Winter term 2020/2021 1. Support Vector Machines (SVM) 2. Soft Margin Support Vector Machines 3. Kernels SVM and Kernels 2 Data Analytics and Machine Learning Support Vector Machines (SVM) Linear
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IN2064 Machine Learning Lecture 5 Lecture 5: Linear Classification Data Analytics and Machine Learning Technical University of Munich Winter term 2020/2021 Copyright By PowCoder代写 加微信 powcoder s scalar is lowercase and not bold s vector is lowercase and bold S matrix is uppercase and bold yˆ predicted class label y actual class label I(a) Indicator
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IN2064 Machine Learning Lecture 12 Lecture 12: Clustering Prof. Dr. ̈nnemann Data Analytics and Machine Learning Technical University of Munich Copyright By PowCoder代写 加微信 powcoder Winter term 2020/2021 Unsupervised learning • Given some unlabeled data {xi} we want to discover latent structure in it. Last week: Dimensionality reduction • Given high-dimensional data x i ∈
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• Chapter: Dimensionality Reduction & Matrix Factorization 1. Introduction 2. Principal Component Analysis (PCA) 3. Singular Value Decomposition (SVD) Copyright By PowCoder代写 加微信 powcoder 4. Matrix Factorization − Motivation & Approach − Regularization & Sparsity − FurtherFactorizationModels 5. Neighbor Graph Methods 6. Autoencoders (Non-linear Dimensionality Reduction) Dimensionality Reduction & Matrix Factorization 64 Data Analytics and
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Machine Learning Lecture 6: Optimization Prof. Dr. ̈nnemann Data Analytics and Machine Learning Technical University of Munich Copyright By PowCoder代写 加微信 powcoder Winter term 2020/2021 Motivation • Many machine learning tasks are optimization problems • Examples we’ve already seen: – Linear Regression w⇤ = arg minW 12 (Xw y)T (Xw y) – Logistic Regression w⇤
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Machine Learning Lecture 10: Dimensionality Reduction & Matrix Factorization Prof. Dr. ̈nnemann Data Analytics and Machine Learning Group Technical University of Munich Copyright By PowCoder代写 加微信 powcoder Winter term 2021/2022 Dimensionality Reduction & Matrix Factorization Data Analytics and Machine Learning • Chapter: Dimensionality Reduction & Matrix Factorization 1. Introduction 2. Principal Component Analysis (PCA) 3.
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Machine Learning Lecture 7: Deep Learning I Prof. Dr. ̈nnemann Data Analytics and Machine Learning Group Technische Universit ̈at Mu ̈nchen Copyright By PowCoder代写 加微信 powcoder Winter term 2020/2021 Introduction Deep Learning 2 Data Analytics and Machine Learning Another look at Logistic Regression We had before: y | x ∼ BernoulliσwT x wTx:=w0 +w1x1 +…+wDxD
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Algorithmic Fairness Motivation: Algorithms influence our lives in many ways • Machine Learning based systems have been used (to automate complex decision) for: Copyright By PowCoder代写 加微信 powcoder • Selecting job applicants • Recidivism prediction and predictive policing • Credit scoring and loans • Facial recognition • Search and recommendations • Machine Translation • …
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Machine Learning Lecture 13: Advanced Topics Prof. Dr. ̈nnemann Data Analytics and Machine Learning Technical University of Munich Copyright By PowCoder代写 加微信 powcoder Winter term 2020/2021 Introduction – Beyond Accuracy Differential Privacy Algorithmic Fairness Advanced Topics 2 Data Analytics and Machine Learning • In the previous lectures we were focusing on models and algorithms and