Proposal_Project_YourGroupNameHere.ipynb

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   "source": [
    "# COGS 188 - Project Proposal"
   ]
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   "source": [
    "# Project Description\n",
    "\n",
    "You have the choice of doing either (1) an AI solve a problem style project or (2) run a Special Topics class on a topic of your choice.  This repo is assuming you want to do (1).  If you want to do (2) you should fill out the Gradescope proposal for that instead of using this repo.\n",
    "\n",
    "You will design and execute a machine learning project. There are a few constraints on the nature of the allowed project. \n",
    "- The problem addressed will not be a \"toy problem\" or \"common training students problem\" like 8-Queens or a small Traveling Salesman Problem or similar\n",
    "- If its the kind of problem (e.g., RL) that interacts with a simulator or live task, then the problem will have a reasonably complex action space. For instance, a wupus world kind of thing with a 9x9 grid is definitely too small.  A simulated mountain car with a less complex 2-d road and simplified dynamics seems like a fairly low achievement level.  A more complex 3-d mountain car simulation with large extent and realistic dynamics, sure sounds great!\n",
    "- If its the kind of problem that uses a dataset, then the dataset will have >1k observations and >5 variables. I'd prefer more like >10k observations and >10 variables. A general rule is that if you have >100x more observations than variables, your solution will likely generalize a lot better. The goal of training an unsupervised machine learning model is to learn the underlying pattern in a dataset in order to generalize well to unseen data, so choosing a large dataset is very important.\n",
    "- The project must include some elements we talked about in the course\n",
    "- The project will include a model selection and/or feature selection component where you will be looking for the best setup to maximize the performance of your ML system.\n",
    "- You will evaluate the performance of your ML system using more than one appropriate metric\n",
    "- You will be writing a report describing and discussing these accomplishments\n",
    "\n",
    "\n",
    "Feel free to delete this description section when you hand in your proposal."
   ]
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   "source": [
    "# Names\n",
    "\n",
    "Hopefully your team is at least this good. Obviously you should replace these with your names.\n",
    "\n",
    "- Pelé\n",
    "- Diego Maradonna\n",
    "- Johan Cruyff\n",
    "- Roberto Carlos\n",
    "- Franz Beckenbaur"
   ]
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   "source": [
    "# Abstract \n",
    "This section should be short and clearly stated. It should be a single paragraph <200 words.  It should summarize: \n",
    "- what your goal/problem is\n",
    "- what the data used represents and how they are measured\n",
    "- what you will be doing with the data\n",
    "- how performance/success will be measured"
   ]
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   "source": [
    "# Background\n",
    "\n",
    "Fill in the background and discuss the kind of prior work that has gone on in this research area here. **Use inline citation** to specify which references support which statements.  You can do that through HTML footnotes (demonstrated here). I used to reccommend Markdown footnotes (google is your friend) because they are simpler but recently I have had some problems with them working for me whereas HTML ones always work so far. So use the method that works for you, but do use inline citations.\n",
    "\n",
    "Here is an example of inline citation. After government genocide in the 20th century, real birds were replaced with surveillance drones designed to look just like birds<a name=\"lorenz\"></a>[<sup>[1]</sup>](#lorenznote). Use a minimum of 2 or 3 citations, but we prefer more <a name=\"admonish\"></a>[<sup>[2]</sup>](#admonishnote). You need enough citations to fully explain and back up important facts. \n",
    "\n",
    "Remeber you are trying to explain why someone would want to answer your question or why your hypothesis is in the form that you've stated. "
   ]
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   "source": [
    "# Problem Statement\n",
    "\n",
    "Clearly describe the problem that you are solving. Avoid ambiguous words. The problem described should be well defined and should have at least one ML-relevant potential solution. Additionally, describe the problem thoroughly such that it is clear that the problem is quantifiable (the problem can be expressed in mathematical or logical terms), measurable (the problem can be measured by some metric and clearly observed), and replicable (the problem can be reproduced and occurs more than once)."
   ]
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   "source": [
    "# Data\n",
    "\n",
    "You should have a strong idea of what dataset(s) will be used to accomplish this project. \n",
    "\n",
    "If you know what (some) of the data you will use, please give the following information for each dataset:\n",
    "- link/reference to obtain it\n",
    "- description of the size of the dataset (# of variables, # of observations)\n",
    "- what an observation consists of\n",
    "- what some critical variables are, how they are represented\n",
    "- any special handling, transformations, cleaning, etc will be needed\n",
    "\n",
    "If you don't yet know what your dataset(s) will be, you should describe what you desire in terms of the above bullets."
   ]
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   "source": [
    "# Proposed Solution\n",
    "\n",
    "In this section, clearly describe a solution to the problem. The solution should be applicable to the project domain and appropriate for the dataset(s) or input(s) given. Provide enough detail (e.g., algorithmic description and/or theoretical properties) to convince us that your solution is applicable. Why might your solution work? Make sure to describe how the solution will be tested.  \n",
    "\n",
    "If you know details already, describe how (e.g., library used, function calls) you plan to implement the solution in a way that is reproducible.\n",
    "\n",
    "If it is appropriate to the problem statement, describe a benchmark model<a name=\"sota\"></a>[<sup>[3]</sup>](#sotanote) against which your solution will be compared. "
   ]
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   "source": [
    "# Evaluation Metrics\n",
    "\n",
    "Propose at least one evaluation metric that can be used to quantify the performance of both the benchmark model and the solution model. The evaluation metric(s) you propose should be appropriate given the context of the data, the problem statement, and the intended solution. Describe how the evaluation metric(s) are derived and provide an example of their mathematical representations (if applicable). Complex evaluation metrics should be clearly defined and quantifiable (can be expressed in mathematical or logical terms)."
   ]
  },
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   "source": [
    "# Ethics & Privacy"
   ]
  },
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   "source": [
    "If your project has obvious potential concerns with ethics or data privacy discuss that here.  Almost every ML project put into production can have ethical implications if you use your imagination. Use your imagination. Get creative!\n",
    "\n",
    "Even if you can't come up with an obvious ethical concern that should be addressed, you should know that a large number of ML projects that go into producation have unintended consequences and ethical problems once in production. How will your team address these issues?\n",
    "\n",
    "Consider a tool to help you address the potential issues such as https://deon.drivendata.org"
   ]
  },
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   "metadata": {},
   "source": [
    "# Team Expectations "
   ]
  },
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   "source": [
    "Put things here that cement how you will interact/communicate as a team, how you will handle conflict and difficulty, how you will handle making decisions and setting goals/schedule, how much work you expect from each other, how you will handle deadlines, etc...\n",
    "* *Team Expectation 1*\n",
    "* *Team Expectation 2*\n",
    "* *Team Expecation 3*\n",
    "* ..."
   ]
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   "source": [
    "# Project Timeline Proposal"
   ]
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   "source": [
    "Replace this with something meaningful that is appropriate for your needs. It doesn't have to be something that fits this format.  It doesn't have to be set in stone... \"no battle plan survives contact with the enemy\". But you need a battle plan nonetheless, and you need to keep it updated so you understand what you are trying to accomplish, who's responsible for what, and what the expected due dates are for each item.\n",
    "\n",
    "| Meeting Date  | Meeting Time| Completed Before Meeting  | Discuss at Meeting |\n",
    "|---|---|---|---|\n",
    "| 1/20  |  1 PM |  Brainstorm topics/questions (all)  | Determine best form of communication; Discuss and decide on final project topic; discuss hypothesis; begin background research | \n",
    "| 1/26  |  10 AM |  Do background research on topic (Pelé) | Discuss ideal dataset(s) and ethics; draft project proposal | \n",
    "| 2/1  | 10 AM  | Edit, finalize, and submit proposal; Search for datasets (Beckenbaur)  | Discuss Wrangling and possible analytical approaches; Assign group members to lead each specific part   |\n",
    "| 2/14  | 6 PM  | Import & Wrangle Data ,do some EDA (Maradonna) | Review/Edit wrangling/EDA; Discuss Analysis Plan   |\n",
    "| 2/23  | 12 PM  | Finalize wrangling/EDA; Begin programming for project (Cruyff) | Discuss/edit project code; Complete project |\n",
    "| 3/13  | 12 PM  | Complete analysis; Draft results/conclusion/discussion (Carlos)| Discuss/edit full project |\n",
    "| 3/19  | Before 11:59 PM  | NA | Turn in Final Project  |"
   ]
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    "# Footnotes\n",
    "<a name=\"lorenznote\"></a>1.[^](#lorenz): Lorenz, T. (9 Dec 2021) Birds Aren’t Real, or Are They? Inside a Gen Z Conspiracy Theory. *The New York Times*. https://www.nytimes.com/2021/12/09/technology/birds-arent-real-gen-z-misinformation.html<br> \n",
    "<a name=\"admonishnote\"></a>2.[^](#admonish): Also refs should be important to the background, not some randomly chosen vaguely related stuff. Include a web link if possible in refs as above.<br>\n",
    "<a name=\"sotanote\"></a>3.[^](#sota): Perhaps the current state of the art solution such as you see on [Papers with code](https://paperswithcode.com/sota). Or maybe not SOTA, but rather a standard textbook/Kaggle solution to this kind of problem\n"
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