Koo Laboratory

Machines, Inference and Algorithms series – Broad Institute https://www.youtube.com/playlist?list=PLlMMtlgw6qNjROoMNTBQjAcdx53kV50cS

Learning from Data (Yaser Abu-Mostafa’s Caltech course) website: http://work.caltech.edu/lectures.html video: https://www.youtube.com/playlist?list=PLD63A284B7615313A

Yann LeCunn’s deep learning course at NYU: Course website: https://atcold.github.io/pytorch-Deep-Learning/ Playlist: http://bit.ly/pDL-YouTube

Deep Unsupervised learning (Pieter Abeel’s Berkeley CS294-158 course) website: https://sites.google.com/view/berkeley-cs294-158-sp19/home video: https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos

Introduction to Computational Biology and bioinformatics (Shirley Liu’s Stat115 course at Harvard) website: https://canvas.harvard.edu/courses/39391 video: https://www.youtube.com/playlist?list=PLeB-Dlq-v6tY3QLdQBA7rwb4a7fK9mLpv

Intro to Biology https://www.edx.org/course/introduction-to-biology-the-secret-of-life-3

Foundations of Computational and Systems Biology (MIT’s 7.91J course) website: https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/ video: https://www.youtube.com/playlist?list=PLUl4u3cNGP63uK-oWiLgO7LLJV6ZCWXac

Deep learning resource Convolutional Neural Networks for Visual Recognition (Stanford’s CS231N course) website: http://cs231n.github.io/ video: https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv

Deep Learning Summer School - MILA (I would skip the reinforcement learning stuff for now) videos: http://videolectures.net/DLRLsummerschool2018_toronto/

Deep Learning Framework Tutorials

Tensorflow https://github.com/edyoda/tensorflow2.0-tutorial https://github.com/Hvass-Labs/TensorFlow-Tutorials

Older tutorials https://github.com/pkmital/tensorflow_tutorials https://github.com/MorvanZhou/Tensorflow-Tutorial

Pytorch https://github.com/MorvanZhou/PyTorch-Tutorial

Machine Learning

Independent study (Harvard CS 181 - Intro to Machine Learning): https://harvard-ml-courses.github.io/cs181-web-2017/#schedule

Bishop code in python https://github.com/ctgk/PRML

Intro to Machine Learning (Andrew Ng’s Stanford course) website: http://cs229.stanford.edu/ video: https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN

Learning from Data (Yaser Abu-Mostafa’s Caltech course) website: http://work.caltech.edu/lectures.html video: https://www.youtube.com/playlist?list=PLD63A284B7615313A Information Theory, Pattern Recognition, and Neural Networks (David Mackay’s University of Cambridge course) video: http://videolectures.net/course_information_theory_pattern_recognition/ book: http://www.inference.org.uk/mackay/itila/

Deep Learning Convolutional Neural Networks for Visual Recognition (Stanford’s CS231N course) website: http://cs231n.github.io/ video: https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv

Natural Language Processing with Deep Learning (Stanford’s CS224N course) website: http://web.stanford.edu/class/cs224n/ video: https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

Introduction to Deep Learning (MIT’s 6.S191 course) website: http://introtodeeplearning.com/ video: https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

Adversarial training tutorial https://adversarial-ml-tutorial.org/

Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow code: https://github.com/ageron/handson-ml2

Yann LeCunn’s deep learning course at NYU: Course website: https://atcold.github.io/pytorch-Deep-Learning/ Playlist: http://bit.ly/pDL-YouTube

Berkeley’s Intro to Deep Learning (Stat 157) book: http://d2l.ai/chapter_introductio (Statn/index.html website: https://courses.d2l.ai/berkeley-stat-157/index.html code: https://github.com/dsgiitr/d2l-pytorch lectures: https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW

https://cs330.stanford.edu/

http://web.stanford.edu/class/cs234/index.html

Machine Learning Textbooks

Goodfellow, Bengio, and Courville - “Deep Learning”

Christopher Bishop’s - “Pattern recognition and machine learning”

Kevin Murphy’s - “Machine Learning: A probabilistic interpretation”

Mackay - “information theory, Inference, and Learning Algorithms”

VC dimentions https://en.wikipedia.org/wiki/Rademacher_complexity