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Contents
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Week 1: |
Introduction of basic terminology in machine learning. Definitions of big data, classification, deep learning, supervised and unsupervised learning. |
Week 2: |
Probability distributions. Moments and Gauss distributions. State vectors and multivariate distributions. Example solution and coding. |
Week 3: |
Parameter estimation. Likelihood function. Bayesian classification. Naive Bayes classifiers. Example solution and coding. Homework 1: Naive Bayes classifiers. |
Week 4: |
Dimension reduction. PCA, LDA, SVD, matrix factorization. Isomap. Example solution and coding. Project topics assignment. |
Week 5: |
Unsupervised learning. Clustering. Mixture of distributions. k-means. EM algorithm. Spectral, hierarchical clustering approaches. Determining the number of clusters. Example solution and coding. Homework 2: Dimension reduction and clustering. |
Week 6: |
Nonparametric approaches. Histograms. Kernel approaches. k-nn. Anomaly detection. Example solution and coding. |
Week 7: |
Midterm. Decision trees. Classification and regression trees. Example solution and coding. |
Week 8: |
Support vector machines. Example solution and coding. Homework 3: Decision trees. |
Week 9: |
Bayesian prediction. Hypothesis testing. Example solution and coding. |
Week 10: |
Ensemble classifiers. Example solution and coding. Homework 4: Classification algorithms. |
Week 11: |
Linear discriminators. Multi-class discrimination. Gradient descent and parameter estimation. Example solution and coding. |
Week 12: |
Introduction to artificial neural networks. Peceptron. Multi-layer perceptrons. Homework 5: Artificial neural network. |
Week 13: |
Activation functions. Backpropagation. Deep learning. Example solution and coding. |
Week 14: |
Reinforcement learning. Project presentations. |
Week 15*: |
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Week 16*: |
Final exam. |
Textbooks and materials: |
E. Alpaydin, Introduction to Machine Learning. MIT, 2014. |
Recommended readings: |
R. O. Duda, P. E. Hart, ve D. G. Stork, Pattern Classification. Wiley, 2000. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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