27938 - پروژه یادگیری ماشین (پایتون)...

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شرح پروژه

- Advance topic in machine learning

- زبانش پایتون

- من یه پروپوزال دارم ک باید از تکنیک های causal inference روش پیاده سازی کنم. هیج بایدی ک چه روشی باید استفاده بشه نداره فقط تهش باید یه گزارش با فرمت مقاله داشته باشم ک بگم من از این تکنیک رو این دیتاست استفاده کردم و این نتایجشه

- چون تعریف پروژه با خودمون بوده کل توضیحات پروژه با خودمون بوده. این ک چه کاری می خوایم بکنیم فقط کافیه یکی از تکنیک های جزوه استفاده بشه مرتبط با کازال اینفرنس و ی ریپورتی داده بشه. اینا جزوه های درس کازال اینفرنسن (فایل زیپ ضمیمه)

- در مورد casual inference: این بهترین مقاله ای بود ک پیدا کردم براش. تکنیک هایی رو هم که میشه استفاده کرد آورده (ضمیمه)

- من توضیحات کازال اینفرنس رو براتون کپی می کنم. حقیقتا ماهیت درس برا خودمم قابل فهم نیست اصن:

The course is intended to be an advanced topics course on recent machine learning methods.
The course will focus on Causal Inference this semester.
AI plays an increasingly prominent role in society since more decisions that were once
made by humans are now delegated to automated systems. These systems are expected to
be efficient, robust, explainable, generalizable, and lead to outcomes agreed upon by
society. However, the current generation of AI/ML systems are almost invariably driven
by probabilistic/statistical machinery. There is a growing understanding that statistical
associations are often devoid of meaningful interpretation and cannot predict what happens
if the environment changes or some external interventions occur, and that robust decision-
making relies on some knowledge of the causal mechanisms underlying the environment.
For instance, an intelligent robot has to know the cause-and-effect relationships in its
environment to plan its course of actions more robustly; a physician needs to understand
the effects of available drugs to design an effective strategy for patients; an economist
needs to envision the relationship between skill sets and the future job market so as to
design a new educational policy more efficiently.
In this course, we will introduce basic concepts, principles, and algorithms of causal
inference. Emphasis will be given to the tradeoff between assumptions (delineated by
current scientific knowledge) and conclusions for standard types of queries, including
associational, causal, and counterfactual. In other words, we will consider the problem of
providing different types of “explanation” for the vast amount of data collected in different
fields of human inquiry, including engineering, medicine, and the empirical sciences. This
problem lies at the heart of current discussions in artificial intelligence, machine learning,
and statistics. The purpose is to expose students to state-of-the-art methods and applications
in a variety of domains and to prepare students for doing research in the area.

این پروژه شامل 3 فایل مهم است، لطفا قبل از ارسال پیشنهاد حتما نسبت به بررسی این فایل اقدام فرمایید.

مهارت ها و تخصص های مورد نیاز

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