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.
MSCBDT5002_FINAL_EXAM Q1. Supervised Outlier Detection (15 points) In this question, you need to use a supervised classification model to find outliers from our given image data set. The data set will contain two types of tags: outliers and inliers. And the main content of the data set is some random scenes with text as the […]
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Microsoft Word – CS504_hw5.doc CS504 Fall 2018 Homework 5 Due: 12/07/2018 by 11:59PM The goal of this homework is to analyze the performance of classification algorithms on several real and synthetic data sets. This will be done in the following steps: First, you will explore the data sets. Next, you will perform a series of
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EECS 391 Introduction to Artificial Intelligence Fall 2018, Programming Assignment 2 (“P2”) Due: Fri Dec 7 before midnight. Total Points: 100 + 20 extra credit In this assignment, you will train a linear classifier on the iris dataset. Like HW5, this will be convenient to do in a language with a good plotting library such
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EECS 391 Introduction to Artificial Intelligence Fall 2018, Written Assignment 5 (“W5”) Due: Tue Nov 27. Submit a single pdf document along with your code for the whole assignment to Canvas before class. You may scan a handwritten page for the written portions, but make sure you submit a single pdf file. Total Points: 100
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COMP6245: Coursework 1 Due date The hand-in date for the assignment is Monday, December 3, 2018 . 2 Introduction You should implement all the exercises for yourself. Tools such as scikit- learn are there for you to use in practical contexts, but for understanding the underpinnings of many of those implementations you need to work
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Structure of the Data: Data Size (rough estimation): 361,162kb (26 column and more than 1,000,000 rows ) Data Fields of the data and Brief Explanation: Crime Code: Indicates the crime committed. Crime Code Description: Defines the Crime Code provided. Victim Age: Two numeric characters Victim Sex: F – Female M – Male X
Lab 4: Belief Propagation This lab is built around the process of identifying the fault with a coffee machine. Your task is to: Given the structure of a graphical model for the state of a coffee machine learn the distributions from data. Implement belief propagation, so you can evaluate the probability of each failure given
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EECE5644 Fall 2018 – Homework 4 This assignment is due on Blackboard by 10:00am ET on Tuesday, November 13, 2018. Please submit your solutions on Blackboard in a single PDF file that includes all math, visual and quantitative results (plots, tables, etc), as well as your code (appended after your answers/solutions for each question). In
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MLNC – Machine Learning & Neural Computation Coursework 1 – Grid World October 25, 2018 To be returned via Blackboard as indicated online. Your coursework should contain: your name, your CID and your degree course at the top of the first page. You text should provide brief analytical derivations and calculations as necessary in-line, so
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CSCC11 Introduction to Machine Learning, Fall 2018 Assignment 2 Due Friday, November 2, 2018, by 11 AM This assignment includes both written work and programming (in Matlab). To begin the programming, download a2starter.tar from the calendar page on the course website. When you untar a2starter.tar the directory A2 is created. Please do not change the
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