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.
CS 189 Introduction to Machine Learning Fall 2017 HW6 You may typeset your homework in latex or submit neatly handwritten and scanned solutions. Please make sure to start each question on a new page, as grading (with Gradescope) is much easier that way! Deliverables: 1. Submit a PDF of your writeup to assignment on Gradescope, […]
机器学习代写：CS 189 Introduction to Machine Learning HW6 Read More »
Computer Science 401 St. George Campus Red team, blue team: Identifying political persuasion on Reddit Introduction This assignment will give you experience with a social media corpus (i.e., a collection of posts from Reddit), Python programming, part-of-speech (PoS) tags, sentiment analysis, and machine learning with scikit-learn. Your task is to split posts into sentences, tag
python-nlp自然语言处理代写: CSC401 Assignment 1 Read More »
ITI 1121. Introduction to Computing II Assignment 1 Learning objectives Applying basic object oriented programming concepts Using arrays to store information Using reference variables Editing, compiling and running Java programs Raising awareness concerning the university policies for academic fraud Introduction Artificial Intelligence is a “hot” topic : every day, you can read about it in the
机器学习java代写：ITI 1121. Introduction to Computing Assignment 1 Read More »
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 ) du = −dα exp−(αu ) du to show that the even central moments of the Gaussian density are E[(x−m)n]
机器学习模式识别代写：ANLY-601 Pattern Recognition Homework 1 Read More »
MLP Courseworks 3 & 4 Due: 2017-02-16 (cw3); 2017-03-16 (cw4) Machine Learning Practical: Courseworks 3 & 4 Release date Friday 27 January 2017 Due dates 1. Baseline experiments (Coursework 3) – 16:00 Thursday 16th February 2017 2. Advanced experiments (Coursework 4) – 16:00 Thursday 16th March 2017 1 Introduction Courseworks 3 & 4 in MLP
机器学习 深度学习 tensorflow代写：MLP Courseworks 3 & 4 Read More »
University of Lincoln School of Computer Science 2016 – 2017 Assessment Item 1 of 2 Briefing Document Title: CMP3036M Data Science Indicative Weighting: 50% Learning Outcomes On successful completion of this component a student will have demonstrated competence in the following areas: LO1 Critically apply fundamental concepts and techniques in data science Overview The objective
R语言机器学习代写：CMP3036M Data Science Assessment Item 1 Read More »
University of Lincoln School of Computer Science 2016 – 2017 Assessment Item 2 of 2 Briefing Document Title: CMP3036M Data Science Indicative Weighting: 50% Learning Outcomes On successful completion of this component a student will have demonstrated competence in the following areas: LO1 Critically apply fundamental concepts and techniques in data science LO2 Utilise state-of-the-art
机器学习数据科学代写：CMP3036M Data Science Assessment Item 2 Read More »
Introduction COMP9444 Neural Networks and Deep Learning Session 2, 2017 Project 3 – Deep Reinforcement Learning Due: Sunday 29 October, 23:59 pm Marks: 15% of final assessment In this assignment we will implement a Deep Reinforcement Learning algorithm on some classic control tasks in the OpenAI AI-Gym Environment. Specifically, we will implement Q-Learning using a
深度学习-deep-learning代写：Deep Reinforcement Learning Read More »
COMP9444 Neural Networks and Deep Learning Session 2, 2017 Project 2 – Recurrent Networks and Sentiment Classification Due: Sunday 8 October, 23:59 pm Marks: 15% of final assessment Introduction You should now have a good understanding of the internal dynamics of TensorFlow and how to implement, train and test various network architectures. In this assignment
深度学习代写： COMP9444 Neural Networks and Deep Learning Read More »
Natural Language Engineering: Assessed Coursework Submission format: You should submit one file that should either be a Jupyter notebook or a zip file containing a Jupyter notebook and the image files that you want to include in the notebook. Due date: You work should be submitted on the module’s Study Direct site before 4pm on
python-自然语言处理代写: Natural Language Engineering: Assessed Coursework Read More »