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.
Note that the 580 students must implement one additional function (K-fold validation). I have decided to provide the list version of this code rather than the array version (the array version is tougher to write but harder to write bugs in). See the questions at the end of the file. Note that this is a […]
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Write an information-theoretic CHOOSE-feature heuristic ; for a decision tree. ; ; Due THURSDAY, October 6, at MIDNIGHT. ; ; (A) I have provided you with the most of the basic code to do a decision tree, ; but the main building algorithm is missing and the CHOOSE-FEATURE function is ; stupid right now —
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Represent the audio signal as a sequence of features, e.g., Mel-frequency cepstral coefficients (MFCCs). (I) Develop and evaluate a conventional activity recognition system based on using the Gaussian Mixture Model (GMM) – perform the training of the model for each activity with corresponding data. (II) Develop and evaluate a GMM-UBM system –
Due: Submission Mechanism: Submission Materials: Assessment Criteria: Introduction 5:00pm, Friday, 29 May, 2015 (but see Late Submission Policy) PDF to Turnitin; code and system outputs on the CIS servers (where appropriate) Written report in PDF, as project 1; code and system outputs as necessary Creativity, Critical Analysis, Soundness, Report Quality COMP30018/COMP90049 Knowledge Technologies, Semester 1
INF 553 – Homework #4 Hierarchical Clustering Due: 11/9/2015, Monday, 11:59pm, to Blackboard In this assignment, you are asked to implement a hierarchical agglomerative clustering algorithm. As shown in class, the algorithm starts by placing each data point in a cluster by itself and then repeatedly merges two clusters until some stopping condition is met.
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COMP 3620/6320 Assignment 4: Reinforcement Learning Semester 1, 2015 Main Details In this assignment you will implement an experiment with a number of temporal-difference (TD) learning methods in examples based on the Grid- world. This assignment will require detailed knowledge of the reinforcement learning problem and TD methods. Before starting, if you have not already
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for those functions. Solve the following tasks, and you should keep in mind that shorter codes have a positive impact on your mark. Tasks: You are going to work with datasets dataOLS.txt and IndustriesUS.txt. The first dataset contains 4 variables: sorted as date, Market excess returns, SMB factor, HML factor and risk-free asset returns. Dataset
Homework Assignment #5 All data sets relevant for this assignment can be found at the UCI Machine Learning Repository, at http://archive.ics.uci.edu/ml/ Problem 1 (45 points) Download three different data sets (of your choice) for binary classification. Plan on taking only the data with real-valued features; however, if some features are categorical, say
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Programming Assignment Due Wednesday, May 6th Sparse (Linear) Logistic Regression Suppose that we have a collection of vectors xi 2 Rn, i = 1, 2, …, N, and that for each i there is a value yi 2 {0, 1}. Given a new value x 2 Rn, we want to predict the value y to
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CS 369 2014 Assignment 3 Due Friday May 29 8:30 pm This assignment has two parts. In the first part, we use a Hidden Markov Model to model secondary structure in protein sequences and implement a couple of algorithms we saw in lectures. In the second part, we simulate sequences down a tree according to
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