Internship - Applied Mathematics Job in Viga Entertainment Technology
Internship - Applied Mathematics
Viga Entertainment Technology
4+ weeks ago
- Bengaluru, Bangalore Urban, Karnataka
- Not Disclosed
- Full-time
Job Summary
1. Understanding of machine learning and image processing including topics such as:
1.1. Linear Regression and gradient descent algorithms
1.2. Decision trees
1.3. Random forest
1.4. Adaboost
1.5. K-Means Clustering
1.6. Neural Networks
1.7. Logistic Regression
1.8. Regularization
1.9. Histogram of Gradients and Scale-Invariant Feature Transforms(SIFT)
1.10. Linear Discriminant Model
1.11. Cross Validation
1.12. Principal Component Analysis
2. Understanding of linear algebra including topics such as:
2.1. Tensors
2.2. Norms
2.3. Matrix decomposition
2.4. Probability theory such as bayesian network and optimization
2.5. Homoscedasticity and Heteroscedasticity
2.6. Derivatives, Jacobian and hassian matrices
2.7. Covariance matrix
2.8. Non negatice least squares
2.9. Singular value decompositions
2.10. Different types of distributions e.g. gaussian, normal etc
2.11. Deformation Gradients
2.12. Gaussian Mixture Model
Job Description
- This internship is open for all students interested in working on cutting edge projects.
- Internship is open for the duration that the student is available.
- End products are related to gaming and entertainment.
- Will be having exciting challenges , we can promise that nothing will be boring in this internship
- Great opportunityto work with some of the best people in the industry
- Requires a very good understanding of linear algebra.
Requirements
GENERAL REQUIREMENTS:1. Understanding of machine learning and image processing including topics such as:
1.1. Linear Regression and gradient descent algorithms
1.2. Decision trees
1.3. Random forest
1.4. Adaboost
1.5. K-Means Clustering
1.6. Neural Networks
1.7. Logistic Regression
1.8. Regularization
1.9. Histogram of Gradients and Scale-Invariant Feature Transforms(SIFT)
1.10. Linear Discriminant Model
1.11. Cross Validation
1.12. Principal Component Analysis
2. Understanding of linear algebra including topics such as:
2.1. Tensors
2.2. Norms
2.3. Matrix decomposition
2.4. Probability theory such as bayesian network and optimization
2.5. Homoscedasticity and Heteroscedasticity
2.6. Derivatives, Jacobian and hassian matrices
2.7. Covariance matrix
2.8. Non negatice least squares
2.9. Singular value decompositions
2.10. Different types of distributions e.g. gaussian, normal etc
2.11. Deformation Gradients
2.12. Gaussian Mixture Model
Benefits
- Stipend is paid for all interns at Viga Entertainmenttechnology
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