Statements (48)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:university
|
| gptkbp:course |
gptkb:CS156
|
| gptkbp:department |
gptkb:Department_of_Computing_and_Mathematical_Sciences
|
| gptkbp:focusesOn |
gptkb:machine_learning
|
| gptkbp:hasLectureNotes |
https://github.com/caltechlibrary/cs156
|
| gptkbp:hasWebsite |
https://github.com/caltechlibrary/cs156
|
| https://www.w3.org/2000/01/rdf-schema#label |
Caltech CS156
|
| gptkbp:languageOfInstruction |
English
|
| gptkbp:level |
graduate
undergraduate |
| gptkbp:location |
gptkb:Pasadena,_California
|
| gptkbp:notableFaculty |
gptkb:Anima_Anandkumar
gptkb:Yisong_Yue gptkb:Pietro_Perona |
| gptkbp:offeredBy |
gptkb:California_Institute_of_Technology
|
| gptkbp:prerequisite |
calculus
probability statistics programming linear algebra |
| gptkbp:topic |
gptkb:Q-learning
gptkb:reinforcement_learning gptkb:kernel_methods gptkb:PCA deep learning neural networks optimization gradient descent probabilistic graphical models supervised learning Bayesian methods bias-variance tradeoff clustering cross-validation decision trees dimensionality reduction ensemble methods feature selection linear regression logistic regression regularization support vector machines unsupervised learning model selection Markov decision processes policy gradients |
| gptkbp:bfsParent |
gptkb:Learning_from_Data
|
| gptkbp:bfsLayer |
6
|