Training Machines to Learn From Experience

from pathlib import Path

DATA_DIR = Path("/kaggle/input")
if (DATA_DIR / "ucfai-core-sp20-reinforcement-learning").exists():
    DATA_DIR /= "ucfai-core-sp20-reinforcement-learning"
    # You'll need to download the data from Kaggle and place it in the `data/`
    #   directory beside this notebook.
    # The data should be here:
    DATA_DIR = Path("data")

Other Meetings in this Series

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GANs are relativity new in the machine learning world, but they have proven to be a very powerful architecture. Recently, they made headlines in the DeepFake network, being able to mimic someone else in real time in both video and audio. There has also been cycleGAN, which takes one domain (horses) and makes it look like something similar (zebras). Come and learn the secret behind these type of networks, you will be surprised how intuitive it is! The lecture will cover the basics of GANs and different types, with the workshop covering how we can generate human faces, cats, dogs, and other cute creatures!

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We've learned about linear and statistical models as well as different training paradigms, but we've yet to think about how it all began. In Cognitive Computational Neuroscience, we look at AI and ML from the perspective of using them as tools to learn about human cognition, in the hopes of building better AI systems, but more importantly, in the hopes of better understanding ourselves.

Contributing Authors

John Muchovej
John Muchovej

Founder of AI@UCF. Researcher in cognitive science and machine learning. Focusing on intuitive physics and intuitive psychology.