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This course focus on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning. It covers topics such as classification and linear regression and ending up with more recent topics like boosting hidden Markov models, and Bayesian networks. It will give the student the basic ideas as well as a bit more formal understanding of how, why, and when they work.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module 01
Introduction, linear classification, perceptron update rule 00:10:00
Perceptron convergence, generalization 00:15:00
Maximum margin classification 00:10:00
Classification errors, regularization, logistic regression 00:10:00
Linear regression, estimator bias and variance, active learning 00:15:00
Active learning (cont.), non-linear predictions, kernals 00:10:00
Kernal regression, kernels 00:10:00
Support vector machine (SVM) and kernels, kernel optimization 00:15:00
Model selection 00:15:00
Model selection criteria 00:10:00
Description length, feature selection 00:10:00
Combining classifiers, boosting 00:10:00
Module 02
Boosting, margin, and complexity 00:10:00
Margin and generalization, mixture models 00:10:00
Mixtures and the expectation maximization (EM) algorithm 00:15:00
EM, regularization, clustering 00:10:00
Clustering 00:10:00
Spectral clustering, Markov models 00:10:00
Hidden Markov models (HMMs) 00:15:00
HMMs (cont.) 00:10:00
Bayesian networks 00:10:00
Learning Bayesian networks 00:10:00
Probabilistic inference 00:10:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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