机器学习作为CS必修课程之一, 一般包括supervised learning监督学习, unsupervised learning 无监督学习和reinforcement learning(RL)增强学习3个大方向.
supervised learning通常需要学习linear regression, decision tree, SVM(support vector machine), logistic regression, naive bayes, random forest,
Neural Networks神经网络等内容.
深度学习deep learning是现在非常热门的方向, 需要学习CNN (convolution neural network), RNN (recurrent neural network)等网络架构. 知识点包括dropout, backpropagation, pooling, convolutional layer等. 深度学习现在已经广泛应用到计算机视觉 (computer vision) 和 NLP (自然语言处理).
unsupervised learning通常要学习principal component analysis (PCA), factor analysis, clustering algorithm such as K-means, EM (Expectation–maximization algorithm), GMM (gaussian mixture model)等.
reinforcement learning一般会学习Q-learning和Deep Q-learning.
ANLY-601 Spring 2018 Assignment 4, Due Tuesday, March 27, 2018 — in class You may use your class notes, the text, or any calculus books — please use no other references (including internet or other statistics texts). If you use Mathematica to derive results, you must include the notebook with your solution so I can […]
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COMP532 Assignment 1 – Reinforcement Learning You need to solve each of the following problems. Problem 1 concerns an example/exercise from the book of Sutton and Barto. You must also include a brief report describing and discussing your solutions to the problems. This work can be carried out in pairs of two persons. o This
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HW4 Deadline: Apr. 23rd 5:59 P.M. (before class) There are 4 options in this homework and you can pick one of them. Option 1: Mahout KMeans The K-Means algorithm is to cluster data points into different partitions based on some distance measures. In this part, you need to do K-Means algorithm in all the three
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2017/18 COM6012 – Assignment 3 Assignment Brief Deadline: 11:59PM on Friday 18 May 2018 How and what to submit Create a .zip file containing three folders. One folder per exercise. Name the three folders: Exercise1, Exercise2, and Exercise3. Each folder should include the following files: 1) the .sbt file, 2) the .scala file(s), 3) the
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Vandalism Detection (WSDM Cup 2017) 1.Project Objectives Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation which can be edited by anyone. Its knowledge is increasingly used within Wikipedia as well as in all kinds of information systems, which imposes high demands on its integrity. Nevertheless, Wikidata frequently gets vandalized, exposing all its
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Machine Learning for Statistics SDGB 7847 – Spring 2017 http://www.cs.columbia.edu/ ̃amoretti/7847 Homework # 2 Due March 12th Problem 1 (10 points) Recall that a quadratic form maps a vector to a scalar and is analogous to a quadratic function in multiple dimensions: Q(x)= 1xTDxdTx+c (1) 2 In the above, x 2 Rn is a vector,
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ANLY-601 Spring 2018 Assignment 3 – Mid-term Exam Due 5:00 pm, Monday, March 5, 2018 Please do all your own work. You may use your class notes, the primary course text, any calculus books, Mathematica (for algebraic manipulations), and your favorite numerical packages. Please do not use the internet. Please write legibly. If you use
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The University of Hong Kong School of Public Health Master of Public Health Advanced Statistical Methods I Applied regression Assignment 1 (submit to Moodle by 11:55pm, Mar 22, 2018) March 12, 2018 Association of invasive meningococcal disease and environmental and virologic exposure Weekly counts of invasive meningococcal disease (IMD) reported cases from country A in
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Fundamentals of Machine Learning: Assignment 1 brief Description You are applying for a job with a data analytics company. As part of the recruitment process, they are asking you to use a Multi-Layer Perceptron to model some data. They are providing you with one dataset (see instructions below regarding downloading the data). The dataset contains
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Project Part 3: A dimension reduction algorithm Andreas Artemiou Due date: 4 May 2018 by 23:59 in Learning Central 1 General purpose of the exercise Write a function(or a set of functions) that performs a dimension reduction on a dataset in a univariate regression or classification setting. 1.1 Procedure of the algorithm First of all
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