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[su_tab title = “Description”]
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[su_tab title = “Program Structure”]
Course Syllabus:
1. Linear Regression with One Variable
2. Linear Algebra Review
3. Linear Regression with Multiple Variables
4. Octave Tutorial
5. Logistic Regression
6. Regularization
7. Neural Networks: Representation
8. Neural Networks: Learning
9. Advice for Applying Machine Learning
10. Machine Learning System Design
11. Support Vector Machines
12. Unsupervised Learning
13. Dimensionality Reduction
14. Anomaly Detection
15. Recommender Systems
16. Large Scale Machine Learning
17. Application Example: Photo OCR
Duration:
10 weeks
Important Date:
Contact Institute
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[su_tab title = “Eligibility”]
- This course is at an undergraduate level, likely situated in second or third year.
Pre-requisites:
- The only major prerequisite is differential calculus.
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[su_tab title =”Faculty”]
- Andrew Ng
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[su_tab title = “Contact”]
[/su_tab]
[/su_tabs]
