Course unit
AI Basics - Machine Learning
Last updated: 22/02/2024
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Course Director(s):
DALMAS Benjamin
General Description:
This course is given in English as it corresponds to part of a Teaching Unit of the International MSc on Cyber Physical and Social Systems (CPS2): AI and IoT
The module gives a theoretical overview of the various forms of machine learning. First, we have a glance at unsupervised techniques that help us understand the data. Then, we focus on various supervised learning systems used in practice such as linear models, non linear models (in particular, neural networks), non parametric models, and support vector machines. We show how ensembles of models can outperform a single model, and we detail the particular case of deep neural networks and deep learning. Finally, we study how agents can learn what to do in the absence of labeled examples of what to do. We see how agents can learn from past experience to change their behavior using reinforcement learning techniques (Q-learning). Labs will be performed using python libraries like scikit-learn, pandas or numpy.
Key words:
Machine learning
neural networks
Reinforcement learning
Number of teaching hours
30
Fields of study
Teaching language
English
Intended learning outcomes
| On completion of the unit, the student will be capable of: |
Classification level |
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Learning assessment methods
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Percentage ratio of group assessment
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Written exam:
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Project submission:
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Individual oral exam:
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Group presentation:
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%
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Individual presentation:
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%
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Group practical exercise:
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%
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Individual practical exercise:
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%
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Group report:
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Individual report:
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Other(s): %
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Programme and content
| Type of teaching activity |
Content, sequencing and organisation |
| Supervised work |
(15h) |
| Labs |
(7h30) |