davidrosenberg/ttml2021fall
DS-GA 3001: Tools and Techniques for Machine Learning (NYU Fall 2021)
This course helps machine learning practitioners understand advanced techniques for building and interpreting models, especially when dealing with interventions or uncertainty. It takes foundational knowledge in machine learning, probability, and statistics, and provides methods for causal inference, reinforcement learning, and model interpretability. Data scientists, ML engineers, or researchers looking to deepen their practical application of ML would use this.
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Use this if you are an experienced machine learning practitioner who needs to apply advanced techniques like causal inference, reinforcement learning, or build more robust and interpretable models in real-world scenarios.
Not ideal if you are new to machine learning, probability, or statistics, as it requires a strong foundational background in these areas.
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Jan 08, 2024
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