Statistical Methods For Data Classification By Examples, Pac Learning, Vc Dimension, Nearest Neighbors Algorithm, Decision Trees ,llinear Separators, Svm Algorithm and The Kernel Method, Convex Optimization With Gradient Descent and Stochastic Gradient Descent, Validation And Model Selection, Generative Models and Naive Bayes, Ensemble Methods, Feature Generation and Selection, Neural Nets,unsupervised Learning# Dimensionality Reduction, Clustering._ Learnng Outcomes# at The End of The Course The Students Will Be Able To# 1. Prepare Data For Machine Learning. 2. Implement Basic Machine Learning Algorithms._ 3. Use Supervised Machine Learning Algorithm and Estimate Their Success, While Using Standard Software Packages and Working With Data._ 4. Analyze Properties of Machine Learning Algorithms, Such As Sample Complexity, Computational Complexity, Overfitting and Underfitting, Pac Learning and Vc Dimension, and Explain The Limitations of These Theories.

Faculty: Computer Science
|Undergraduate Studies |Graduate Studies
Pre-required courses

(94412 - Probability (advanced) and 234125 - Numerical Algorithms) or (104034 - Introduction to Probability H and 234125 - Numerical Algorithms)

Course with no extra credit

236756 - Introduction to Machine Learning

Course with no extra credit (contains)

46195 - Machine Learning 96411 - Machine Learning 1