Introduction to Machin Learning
Concept-learning-1
Concept-learning-2
Concept-learning-3
Concept-learning-4
Decision-Tree-1
Decision-Tree-2
Decision-Tree-3
Ensemble-1
Ensemble-2
Ensemble-3
Information-theory-1
Information-theory-2
Parametric-methods-1-MLE
Parametric-methods-2-bayesian
Parametric-methods-3-regression
Parametric-methods-4-model-selection
first-words
introduction_lt
review_ml-1
review_ml-2
vc-dimension-1
pac-learning1
pac-learning2
concentration-inequalities-1
concentration-inequalities-2
concentration-inequalities-3
technical-presentation1.pdf
technical-presentation2.pdf
technical-presentation4.pdf
workshop-technical-writting.pdf
template.pdf
template.docx
lesson1-unit1-section1
lesson1-unit1-section2
lesson9-unit9-section1
lesson9-unit9-section2
lessona-unit10-section1
lessona-unit10-section2
lessonb-unit11-section1
lessonb-unit11-section2
lessonc-unit12-section1
lessonc-unit12-section2
project-ideas.rar
1-non_term-0-first-words.pdf
2-mid-term-1-introduction.pdf
2-mid-term-2-familiarize-with-concepts.pdf
2-mid-term-3-laplace.pdf
2-mid-term-4-state-space-model.pdf
2-mid-term-5-block-diagram-signal-flow-graph.pdf
3-final-term-6-performance-of-feedback-systems .pdf
3-final-term-7-stability .pdf
3-final-term-8-root-locus .pdf
3-final-term-9-pid-controller .pdf
