edu . ntu . https://www.csie.ntu.edu.tw/~htlin/course/mlfound18fall/ Topics This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning. It is also placed 1st amongst the world’s best young universities. NTU has about 33,000 students in the colleges of engineering, science, business, education, humanities, arts, social sciences. Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. ), Learning representations by back-propagating errors (Rumelhart, Hinton, and Williams), On the Momentum Term in Gradient Descent Learning Algorithms (Qian), Adam: A Method for Stochastic Optimization (Kingma and Ba), notes on deep learning (in the ones last week), A linear ensemble of individual and blended models for music rating prediction (Chen et al. These interactive elements and features include: Video lectures; Social interaction https://www.csie.ntu.edu.tw/~htlin/ml20fall/screencast.php, Theory of Generalization :: Restriction of Break Point, Theory of Generalization :: Bounding Function: Basic Cases, Theory of Generalization :: Bounding Funciton: Inductive Cases, Theory of Generalization :: A Pictorial Proof, Matrix Factorization Techniques for Recommender Systems (Koren, Bell and Folinsky), Machine Learning and Data in Big Tech Companies, A linear ensemble of individual and blended models for music rating prediction (Chen et al. We offer courses in land and animal-based subjects, and the creative arts. Read More. ), Learning representations by back-propagating errors (Rumelhart, Hinton, and Williams), On the Momentum Term in Gradient Descent Learning Algorithms (Qian), Adam: A Method for Stochastic Optimization (Kingma and Ba), Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava, Hinton, Krizhevsky, Sutskever and Salakhutdinov), neural networks, matrix factorization (unfinished parts), decision tree (selected) and random forest (selected), gradient boosted decision tree; deep learning basics (selected), modern deep learning: initialization, optimization, regularization, Last updated at CST 17:14, January 19, 2021, TAs and TA hour: html_ta AT csie . tw, Si-An Chen, D09, Mondays 10:00-11:00, CSIE R536, Chi-Pin Huang, B07, Mondays 14:00-15:00, CSIE Basement (Red Sofa), Yu-Chu Yu, R09, Tuesdays 09:00-10:00, CSIE R536, Yi-Hung Chiu, B05, Wednesdays 09:00-10:00, CSIE Basement (Red Sofa), Wei-I Lin, B05, Wednesdays 17:30-18:30, CSIE R536 (call 02-33664888x536 if you cannot go to the 5th floor), Yu-Hsiang Huang, B07, Thursdays 09:00-10:00, CSIE Basement (Red Sofa), Yu-Chen Lin, B06, Thursdays 11:00-12:00, CSIE Basement (Red Sofa), Time: Tuesdays 10:20 to 12:10; Fridays 10:20 to 12:10, Machine Learning Foundations: 100% homework by homework sets 1-4 (tentative), Machine Learning Techniques: 50% homework by homework sets 5-6, 50% project (tentative). Course Description. Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. tw, Sheng-Feng Wu: Tuesdays 10:00--11:00 Online, Ching-Yuan Pai: Wednesdays 9:00--10:00, Online, Si-An Chen: Wednesdays 14:00--15:00, CSIE R536, Chien-Ming Yu: Thursdays 9:00--10:00, Online, Grading: 70% homework, 30% project (tentative). This course is intended to introduce you to a broad introduction of artificial intelligence, machine learning and in particular in the aspect of neural networks. Machine Learning course in National Taiwan University - r03922123/ML_NTU It will introduce several major popular state-of-the-art neural networks architectures as well as deep learning implementation environments. Offered by National Taiwan University. Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. Machine Learning (2017,Fall) Machine Learning and having it deep and structured (2017,Fall) Machine Learning (2017,Spring) Machine Learning and having it deep and structured (2017,Spring) Machine Learning (2016,Fall) Linear Algebra (2016,Spring) Machine Learning and having it … Study with NTU, and you’ll get the best of both worlds — the friendliness of a college community, with university-level facilities and teaching. Course Description. Unsupervised Learning: Deep Auto-encoder pdf, pptx, video (2017/04/20) Unsupervised Learning: Word Embedding pdf, pptx, video (2017/04/27) Unsupervised Learning: Deep Generative Model pdf, pptx, video (2017/04/27) Transfer Learning pdf, pptx, video (2017/05/03) Nanyang Technological University, Singapore. Techniques and methods for extracting information and knowledge from large amounts of data. 課程網頁: http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html MachineLearningMoocNotes. Course Code CE/CZ4041 Course Title Machine Learning Pre-requisites CE/CZ1011: Engineering Mathematics I CE/CZ1007: Data Structures No of AUs 3 . Youtube上的机器学习课程《Machine Learning》的学习笔记,NTU的Hung-yi Lee(李宏毅)老师主讲。 We firmly believe that open access to learning is a powerful socioeconomic equalizer. Course Description. Read More Mangroves at risk if carbon emissions not reduced by 2050, international scientists predict. Fundamentals of Machine Learning [1.5AUs] This course covers essential concepts of machine learning and various supervised learning and unsupervised learning algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor (K-NN) classifiers, decision tree, K … This repository contains my code for the assignments in the 'Machine Learning Techniques' course from National Taiwan University on Coursera. Click here to learn more. This course will introduce the principles of various fundamental machine learning techniques and their applications in data mining, computer vision specifically in the biomedical domain. Whether you’re focused on finding a job or progressing on … In case-based reasoning the integration of learning and problem solving is focused. There are a number of core NLP tasks and machine learning models behind NLP applications. Our online courses are designed to immerse you in the material to make you feel like you’re in the classroom. Common machine learning methods for classification, prediction and clustering Decision tree learning (ID3 and variants thereof) ... To discover more about NTU’s online courses, complete our online form or call the admissions office on 0800 032 1180 (UK) or +44 (0)115 941 8419 (International). Offered by Stanford University. Practicum module, MH680… This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning. ), A short introduction to boosting (Freund and Schapire), Classification and regression trees (overview of decision tree by Loh), Classification and regression trees (book of CART by Breiman et al. M6236 Manufacturing Control and Automation. ), Greedy Function Approximation: A Gradient Boosting Machine (Friedman), Deep sparse rectifier neural networks (Glorot, Bordes and Bengio), Rectifier Nonlinearities Improve Neural Network Acoustic Models (Maas, Hannun and Ng), Delving Deep into Rectifiers: Surpassing Human-Level Performance on Image Net Classification (He, Zhang, Ren and Sun), Understanding the difficulty of training deep feedforward neural networks (Gloret and Bengio), Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (He et al. The knowledge discovery process. 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