curriculum
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curriculum [2023/04/21 09:48] cani [MASTER 1] moved before "Master 2" |
curriculum [2023/04/27 10:31] scornet [Scientific courses, period 1 (October - December)] |
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| ==== Scientific courses, period 1 (October - December) ==== | ==== Scientific courses, period 1 (October - December) ==== | ||
| - | **MAP/INF631 - Deep Learning (48h, 5 ECTS), Erwan Scornet (EP)** (contact: erwan.scornet@polytechnique.edu) | + | **MAP/INF631 - Deep Learning (48h, 4 ECTS), Erwan Scornet (EP)** (contact: erwan.scornet@polytechnique.edu) |
| Deep Learning is one key element of modern data science. This course will explore several instances of Deep Neural Networks, each one being specifically adapted to solve a particular learning task (classification, image recognition, text mining, dimensionality reduction). An introduction to current research topics on neural network will be presented during the last part of the course. | Deep Learning is one key element of modern data science. This course will explore several instances of Deep Neural Networks, each one being specifically adapted to solve a particular learning task (classification, image recognition, text mining, dimensionality reduction). An introduction to current research topics on neural network will be presented during the last part of the course. | ||
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| **MAP/INF641 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) | **MAP/INF641 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) | ||
| - | Reinforcement learning (RL) is of increasing relevance today in science and industry, for developing autonomous agents in areas where dynamic decision making and adaptation is required; including games, energy systems, recommendation engines, finance, logistics, and auto-tuning the parameters of large and powerful computer models (for NLP, computer vision, etc). This course will take a deep look at the foundations of RL, the main learning paradigms (temporal-difference learning, Monte Carlo, and policy gradients methods), their modern state-of-the-art variants, and how they can be enhanced and deployed with deep-learning architectures to tackle problems at a scale found in the real world. We will also take a look at specialized topics such inverse reinforcement learning. | + | Reinforcement learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, and for auto-tuning the parameters of other learning frameworks. This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods). We will explore them further, and study modern state-of-the-art variants (such as proximal policy optimization), with a focus on developing RL solutions with deep neural architectures suited to modern applications. We will also take a look at specialized topics such inverse reinforcement learning. |
| **INF641 - Introduction to the verification of neural networks (24h, 2 ECTS), Eric Goubault (EP), Sylvie Putot (EP)** (contact: sylvie.putot@polytechnique.edu) | **INF641 - Introduction to the verification of neural networks (24h, 2 ECTS), Eric Goubault (EP), Sylvie Putot (EP)** (contact: sylvie.putot@polytechnique.edu) | ||
curriculum.txt ยท Last modified: 2023/06/22 18:41 by scornet
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