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
|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 criteria||00:10:00|
|Description length, feature selection||00:10:00|
|Combining classifiers, boosting||00:10:00|
|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|
|Spectral clustering, Markov models||00:10:00|
|Hidden Markov models (HMMs)||00:15:00|
|Learning Bayesian networks||00:10:00|
|Submit Your Assignment||00:00:00|
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