33 lines
No EOL
13 KiB
Markdown
33 lines
No EOL
13 KiB
Markdown
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| Week | Ready | Main item | Classes | Student work | | |
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| 1 | Yes | Python Prep | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies | - Python prep | | |
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| 2 | Yes | Case 1 | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1. | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1. | | |
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| 3 | | Case 1 | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics) | | |
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| 4 | | Case 1/2 | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1 | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) | | |
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| 5 | | Case 2 | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2 | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html) | | |
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| 6 | | Case 2 | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2 | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf) | | |
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| 7 | | Case 2/3 | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2 | - Work on case 2 <br/> - Read case 3 | | |
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| 8 | | Case 3 | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3 | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) | | |
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| 9 | | Case 3 | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3 | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0) | | |
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| 10 | | Case 3 | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation | - Work on case 3 | | |
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| 11 | | | - Exam | - Deliver case 3 | | |
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| Week | Ready | Main item | Classes | Student work | | |
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| ---- | ----- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- | --- |
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| 1 | Yes | Python Prep | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies | - Python prep | | |
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| 2 | Yes | Case 1 | - L3: Introduction to optimization<br/> - L4: Case 1 Discussion | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1. | | |
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| 3 | | Case 1 | - L5: Introduction to simulation: What is it, When do we use it, Types of simulation <br/> - L6: Simulation Methodology<br/> | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics) | | |
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| 4 | | Case 1/2 | - L7: Case 1 Workshop <br/> - L8: Simulation-based Optimization I | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) | | |
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| 5 | | Case 2 | - L9: Case 2 Discussion <br/> - L10: Simulation-based optimization II: GA | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html) | | |
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| 6 | | Case 2 | - L11: Challenges in real-world usage. Stochastic vs Traditional Optimization <br/> - L12: Case 2 Workshop | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf) | | |
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| 7 | | Case 2/3 | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2 | - Work on case 2 <br/> - Read case 3 | | |
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| 8 | | Case 3 | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3 | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) | | |
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| 9 | | Case 3 | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3 | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0) | | |
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| 10 | | Case 3 | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation | - Work on case 3 | | |
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| 11 | | | - Exam | - Deliver case 3 | | | |