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Statistical Methods For Data Classification By Examples, Pac Learning, Vc Dimension, Nearest Neighbors Algorithm, Decision Trees, Linear 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. Learning Outcomes# By The End Of The Course, The Student Will Be Able# 1. to Prepare Data For Machine Learning# Feature Generation, Feature Selection, Separation to Training, Testing and Validation. 2. to Use Common Supervised and Unsupervised Learning Algorithms, While Using Standard Software Packages and Real Datasets. 3. to Understand The Mathematical Principles Underlying The Taught Material, Including Sample Complexity, Computational Complexity, Overfitting, Under Fitting, Pac Learning, Vc Dimension and The Limitation 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

46195 - Machine Learning 236766 - Introduction to Machine Lerning

• Machine learning : a probabilistic perspective - Murphy, Kevin P.
• Pattern classification - Duda, Richard O.
• Pattern recognition and machine learning - Bishop, Christopher M.
• The elements of statistical learning : data mining, inference, and prediction - Hastie, Trevor
• The Elements of Statistical Learning Data Mining, Inference, and Prediction - Hastie, Trevor.
• Understanding machine learning : from theory to algorithms - Shalev-Shwartz, Shai