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Introduction to Machine learning and Deep learning - 5PMBMLD0

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  • Number of hours

    • Lectures : 0
    • Tutorials : 8.0
    • Laboratory works : 12.0
    • Projects : 0
    • Internship : 0
    • Written tests : 0
    ECTS : 2.0

Goals

Learn some basic algorithms for Machine Learning and deep learning
Learn to design the right Machine Learning method according to a given dataset
Learn to evaluate the performances on a Machine Learning system

Contact Alice CAPLIER

Content

1. Introduction to deep learning
2. K-NN classification
3. Bayesian classification
4. Error Metrics
5. Linear regression
6. Logistic Regression
7. Advices for Applying Machine Learning
8. SVM Classifier
9. K-mean clustering and diemnsion reduction
10. Neural Networks
11. Deep learning



Prerequisites

Matlab or Python language
1st year mathematics course

Tests

Continuous assessment
Reports



100% CC

Additional Information

Curriculum->Double Diploma BIOMED - Structural->Semester 9
Curriculum->Double Diploma BIOMED - N2BIO->Semester 9
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->BIOMED->Semester 9

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Date of update October 14, 2019

Université Grenoble Alpes