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.
1 Instructions QBUS3830 Advanced Analytics Semester 2, 2018 Homework Task 1 Using the code scaffold and data provided: Implement a Python class for a logistic regression. The class should compute the MLE numerically and be able to compute predictions for the class label and probabilities. Follow the instructions in the notebook. Run the test code […]
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One of the challenges that we are currently facing is how to identify the language of the file. File extensions are a good way of identifying the language that has been used but there are some situations when a developer uses a non-standard file extension, such as the name of the component it relates to
CSCI 567, Fall’18 General instructions Homework #3 Programming Assignments Due: 11:59 pm, October 21, 2018 Starting point Your repository will have now a directory “P3/”. Please do not change the name of this repository or the names of any files we have added to it. Please perform a to retrieve these files. You will find
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CSCI 567 Homework #2 Programming Assignments Due: 11:59 pm, Oct. 7th, 2018 General Instructions Starting point Your repository will now have a directory ‘homework2/’. Please do not change the name ofthisrepositoryorthenamesofanyfileswehaveaddedtoit.git pullandyouwillfindthefollowing: Python scripts which you need to amend: – logistic.py – dnn misc.py – dnn cnn 2.py Python scripts which you are not allowed
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COMP6714 18s2 Project Stage 2: Modify a baseline model of hyponymy classification Deadline and Late Penalty The project deadline is 23:59 26 Oct 2018 (Fri). Late penalty is -10% each day for the first three days, and then -20% each day afterwards. Objective As explained in stage 1, in this project you need to build a
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Homework 1 Due Friday, 9/28 by 5pm Turn in to UVA Collab as a PDF Instructions: Complete the following homework. Your answers should be turned into collab on a single typed PDF document. Homework submitted as anything but a PDF will not be accepted. Code should be provided in a separate R file. R simulation
CSC411 Fall 2018 Homework 1 Homework 1 Deadline: Wednesday, Sept. 26, at 11:59pm. Submission: You need to submit two files through MarkUs1: • Your answers to Questions 1(a), 1(b), and outputs requested for Question 2, as a PDF file titled hw1_writeup.pdf. You can produce the file however you like (e.g. LATEX, Microsoft Word, scanner), as
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STA 5106: Homework Assignment #4 1. PCA and Images: Consider the problem of analysis of images. Each (gray scale) image can be thought of as a matrix of numbers, say I ∈Rm1xm2 . We can rewrite this matrix as a long vector X ∈Rm1m2 . Setting n = m1m2, we want to use PCA to
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Question 1 (Multiple Linear Regression, 3.0 points) Consider the multiple linear regression model to regress the logarithm of “Course_eval” on all the other variables in the dataset (please do not consider the interaction terms for now). Please answer the following questions in the answer sheet. a) (1 point) Please use R to fit the model.
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Problem Set 1: Linear Regression To run and solve this assignment, one must have a working IPython Notebook installation. The easiest way to set it up for both Windows and Linux is to install Anaconda. Then save this file to your computer (use “Raw” link on gist\github), run Anaconda and choose this file in Anaconda’s file
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