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.
COMP9417 18s1 Assignment 2 – project topics 0: Self-proposed The objective of this topic is to propose a machine learning problem, source the dataset(s) and implement a method to solve it. This will typically come from an area of work or research of which you have some previous experience. Topic 0: Propose your own topic […]
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COMP9318 (18S1) ASSIGNMENT 1 DUE ON 23:59 23 MAY, 2018 (WED) Q1. (40 marks) Consider the following base cuboid Sales with four tuples and the aggregate function SUM: Location, Time, and Item are dimensions and Quantity is the measure. Suppose the system has built-in support for the value ALL. (1) List the tuples in the
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## Instructions 1. This note book contains instructions for **COMP9318-lab3**. * You are required to complete your implementation in a file `submission.py` provided along with this notebook. * You are not allowed to print out unnecessary stuff. We will not consider any output printed out on the screen. All results should be returned in appropriate
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Assessment 2 Latent Variables and Neural Networks Objectives This assignment consists of three parts (A,B,C), which cover latent variables models and neural networks (Modules 4 and 5). The total marks of this assessment is 100. Part A. Document Clustering In this part, you solve a document clustering problem using unsupervised learning algorithms (i.e., soft and
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## Instructions 1. This note book contains instructions for **COMP9318-lab5**. * You are required to complete your implementation in a file `submission.py` provided along with this notebook. * You are not allowed to print out unnecessary stuff. We will not consider any output printed out on the screen. All results should be returned in appropriate
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COMP9318-Specs 2018/5/24 下午5)05 COMP-9318 Final Project Instructions: This note book contains instructions for COMP9318 Final-Project. You are required to complete your implementation in a file submission.py providedalong with this notebook. You are not allowed to print out unnecessary stuff. We will not consider any outputprinted out on the screen. All results should be returned
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Default project Fake News Challenge Stage 1 (FNC-1): Stance Detection http://www.fakenewschallenge.org FNC-1 Github repositories: https://github.com/FakeNewsChallenge Project Description The Project description has been adapted from the description on the FNC-1 website (http://www.fakenewschallenge.org). Fake news, defined by the New York Times as “a made-up story with an intention to deceive”1, often for a secondary gain, is arguably
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ANLY-601 Pattern Recognition Homework 1 Due Tuesday, January 29, 2018 Use only your course notes — no internet or texts. 1. Moments of Gaussian Densities (10 points) Consider the one-dimensional Gaussian pdf Use the fact that and the identity 1 (x−m)2 p(x)=√2πσ2 exp− 2σ2 . ∞2π exp−(αu ) du = α −∞ 22d2 u exp−(αu
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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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ANLY-601 Pattern Recognition Homework 2 Due Thursday, Feb. 15, 2018 Use only your course notes, integral tables or Mathematica. 1. Linear Gaussian Systems. (12 points) Let x be a Gaussian distributed scalar random variable with mean zero and variance σx2. Let y be related to x by y = Ax + μ + ε (1)
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