curriculum
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curriculum [2023/04/25 15:37] cani [Scientific Courses, period 2 (January - March)] |
curriculum [2023/06/22 18:40] scornet [Scientific Courses, period 2 (January - March)] |
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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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| ==== Scientific Courses, period 2 (January - March) ==== | ==== Scientific Courses, period 2 (January - March) ==== | ||
| - | **MAP/INF641 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) | + | **INF649 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) |
| 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. | 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. | ||
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| - | ==== Final project (April to September - 30 ECTS) ==== | + | ==== Final project (April to September - 32 ECTS) ==== |
| MAP/INF690 - Internship: 5 to 6 months project, either in the R&D department of a company or in a research lab. | MAP/INF690 - Internship: 5 to 6 months project, either in the R&D department of a company or in a research lab. | ||
curriculum.txt ยท Last modified: 2023/06/22 18:41 by scornet
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