ELL409: Difference between revisions
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| credits = 4 | | credits = 4 | ||
| credit_structure = 3-0-2 | | credit_structure = 3-0-2 | ||
| pre_requisites = MTL106, COL106 | | pre_requisites = [[MTL106]], [[COL106]] | ||
| overlaps = ELL784, ELL789, COL341/COL774 | | overlaps = [[ELL784]], [[ELL789]], [[COL341]]/[[COL774]] | ||
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== ELL409 : Machine Intelligence and Learning == | == ELL409 : Machine Intelligence and Learning == | ||
Introduction to machine intelligence and intelligent agents; problem solving; knowledge representation and reasoning (logical and probabilistic); need for learning; basics of machine learning; Decision Trees; Rule-based models; linear learning models; Support Vector Machines; Artificial Neural Networks; Deep Learning; Probabilistic Modelling; Naive Bayes; Reinforcement Learning; Clustering; Feature Selection; Principal Component Analysis; Combining models; Philosophical issues in intelligence and learning. Substantive implementation assignments or a term project involving design of an intelligent learning-based system. | Introduction to machine intelligence and intelligent agents; problem solving; knowledge representation and reasoning (logical and probabilistic); need for learning; basics of machine learning; Decision Trees; Rule-based models; linear learning models; Support Vector Machines; Artificial Neural Networks; Deep Learning; Probabilistic Modelling; Naive Bayes; Reinforcement Learning; Clustering; Feature Selection; Principal Component Analysis; Combining models; Philosophical issues in intelligence and learning. Substantive implementation assignments or a term project involving design of an intelligent learning-based system. | ||
Latest revision as of 16:31, 14 April 2026
| ELL409 | |
|---|---|
| Machine Intelligence and Learning | |
| Credits | 4 |
| Structure | 3-0-2 |
| Pre-requisites | MTL106, COL106 |
| Overlaps | ELL784, ELL789, COL341/COL774 |
ELL409 : Machine Intelligence and Learning
Introduction to machine intelligence and intelligent agents; problem solving; knowledge representation and reasoning (logical and probabilistic); need for learning; basics of machine learning; Decision Trees; Rule-based models; linear learning models; Support Vector Machines; Artificial Neural Networks; Deep Learning; Probabilistic Modelling; Naive Bayes; Reinforcement Learning; Clustering; Feature Selection; Principal Component Analysis; Combining models; Philosophical issues in intelligence and learning. Substantive implementation assignments or a term project involving design of an intelligent learning-based system.