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.
CS440/ECE448 Fall 2016 Assignment 3: Naive Bayes Classification Due date: Monday, November 14, 11:59:59 PM The goal of this assignment is to implement Naive Bayes classifiers as described in this lecture and to apply it to the task of classifying visual patterns and text documents. As before, you can work in teams of up to three people […]
python机器学习代写: CS440/ECE448 Assignment 3: Naive Bayes Classification Read More »
Predict the outcome of EURO 2016 soccer matches for fun and profit Your task is to design machine learning models to predict the outcome of EURO 2016 soccer matches. Briefly explain how you choose the features and the models and how accurate is the model. The data is included in “data.csv” (we’ve also provided
python机器学习代写: Predict the outcome of EURO 2016 soccer matches for fun and profit Read More »
MLP Coursework 2 Due: 24 November 2016 Machine Learning Practical: Coursework 2 Release date: Wednesday 2nd November 2016 Due date: 16:00 Thursday 24th November 2016 Introduction The aim of this coursework is to use a selection of the techniques covered in the course so far to train accurate multi-layer networks for MNIST classification. It is
python深度学习代写: MLP Coursework 2 Read More »
University of Toronto, Department of Computer Science CSC 485/2501F—Computational Linguistics, Fall 2016 Assignment 3 Due date: 23:59, Wednesday 7 December 2016, at the course drop box in BA 2220. Late assignments will not be accepted without a valid medical certificate or other documen- tation of an emergency. This assignment is worth either 25% (CSC 2501)
prolog自然语言处理代写: CSC 485/2501F—Computational Linguistics Assignment 3 Read More »
Problem description There are three data files contains the information about 260+ people in- cluding PD patients and controls. In PD research, it is believed that PD has several sub-types, but currently from our data we don’t have those types infor- mation. An interesting question is that can we use machine learning/ data analysis methods
python-机器学习代写: K-NN, K-means, kernel estimation Read More »
1. 2. 3. 4. 3. Create a PDF document containing answers to the homework questions. Include source code for the program you write. Compress all of the files specified into a .zip file. Name the file in the following manner, firstname_lastname_hw7.zip. Submit this .zip file via Canvas by the date specified on Canvas. EECS 349
机器学习代写: EECS 349 (Machine Learning) Homework 8 Read More »
#| ;;;;;; Assignment 4 ; Write a two-layer backpropagation neural network. ; ; Due THURSDAY, October 20, at MIDNIGHT. ; 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
lisp代写: CS 480 / 580 Assignment 4 Read More »
Department of Computing and Information Systems The University of Melbourne COMP30018/90049 Knowledge Technologies, Semester 2 2016 Due: Submission: Assessment criteria: Marks: Overview Project 1: Misspelled Location Names Friday 02 September 12pm Source code, README to the MSE servers; PDF Report to Turnitin Critical Analysis; Soundness; Report Quality; Creativity. COMP30018: The project will be marked out
机器学习代写: COMP30018/90049 Knowledge Technologies: Project 1: Misspelled Location Names Read More »
Department of Computing and Information Systems The University of Melbourne COMP30018/COMP90049 Knowledge Technologies, Semester 2 2016 Project 2: Geolocation of Tweets with Machine Learning Due: Submission: Assessment Criteria: Marks: Introduction Stage I: 12pm noon (midday), Friday 14 October 2016 Stage II: 11pm (late night), Thursday 20 October 2016 All times Melbourne time. Test data predictions,
机器学习-weka代写: COMP30018/COMP90049 Knowledge Technologies Read More »
1) Boosting (3 points) The AdaBoost algorithm is described in the paper “A Brief Introduction to Boosting” by Robert Schapire. You have been provided this paper on the course website. This algorithm learns from a training set of input/output instances{(x1, y1),…,(xm , ym )}where X is an arbitrary set of inputs, xi ∈ X and
machine learning代写：EECS 349 (Machine Learning) Homework 8 Read More »