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COMPUTER ENGINEERING (ENGLISH) PROGRAMME
COURSE DESCRIPTION
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Name of the Course Unit
| Code
| Year
| Semester
| In-Class Hours (T+P)
| Credit
| ECTS Credit
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INTRODUCTION TO MACHINE LEARNING |
TEL419 |
4 |
8 |
3+0 |
3.0 |
6.0 |
Weekly Course Contents and Study Materials for Preliminary & Further Study |
Week |
Topics (Subjects) |
Preparatory & Further Activities |
1 |
Introduction, linear classification, perceptron update rule |
No file found
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2 |
Perceptron convergence, generalization. Maximum margin classification. |
No file found
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3 |
Classification errors, regularization, logistic regression |
No file found
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4 |
Linear regression, estimator bias and variance, active learning, non-linear predictions |
No file found
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5 |
Kernal regression, kernels, Support vector machine (SVM) and kernels, kernel optimization |
No file found
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6 |
Kernal regression, kernels, Support vector machine (SVM) and kernels, kernel optimization |
No file found
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7 |
midterm exam |
No file found
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8 |
Model selection,Model selection criteria,Description length, feature selection |
No file found
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9 |
Combining classifiers, boosting, Boosting, margin, and complexity |
No file found
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10 |
Margin and generalization, mixture models
Mixtures and the expectation maximization (EM) algorithm |
No file found
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11 |
EM, regularization, clustering, Spectral clustering, Markov models |
No file found
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12 |
Hidden Markov models (HMMs) |
No file found
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13 |
Bayesian networks
Learning Bayesian networks |
No file found
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14 |
Probabilistic inference |
No file found
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