Tutorial-resources.md

Tutorials for getting started with machine learning techniques:

Off-the-shelf tools:

Weka: a software package for analyzing data with selected algorithms link

Tensorflow: end-to-end package for Machine Learning link

OpenCV: a software package for Machine Learning algorithm implementation link

Keras: neural networks and deep learning library link

Scikit-Learn: Machine learning in Python link

Major Venues

Distill (open journal) link

arXiv stat.ML (preprints) link

The Gradient link

DeepAI link

• social network, publishing repository, and resource.

Medium blog Machine Learning category link

IEEE Transactions on Pattern Analysis and Machine Intelligence (journal) link

Data Mining and Knowledge Discovery (journal) link

Journal of Machine Learning Ressearch (journal) link

Pre-trained Models (Deep Learning)

ResNet link

AlexNet link

Inception link

GPT-2 link

DeepLab link

Mask R-CNN link

UNet link

Pre-trained Model Zoo link

Special Topics Tutorials: math- and non-code-oriented

Naive Bayes classification link

Neural Networks (course at Universite de Sherbrooke) link

Deep Learning (Goodfellow, Bengio, and Courville) link

Evolution Strategies link

Artificial Intelligence and Deep Learning link

Open Questions about Generative Adversarial Networks (GANs) link

Bengio-Marcus debate notes link

Innateness (representation) vs. a Data-driven Blank Slate

"Blobs" vs. "symbols" debate: Intuition Machines versus Algebraic Minds. Intuition Machines Medium blog, January 6, 2018. link

The Current State of AI. Gary Marcus Medium blog, October 12, 2019. link

Marcus, G. (2018). Innateness, AlphaZero, and Artificial Intelligence. arXiv, 1801.05667.

Brooks, R.A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.

Zador, A.M. (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications, 10, 3770.

Marcus, G. (2018). Bengio v Marcus, and the Past, Present and Future of Neural Network Models of Language. Machine Learning Medium, October 28.

Garnelo, M. and Shanahan, M. (2019). Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences, 29, 17–23.

Physical Laws in Neural Networks

Lin, H.W., Tegmark, M., and Rolnick, D. (2016). Why Does Deep and Cheap Learning Work So Well? Journal of Statistical Physics, doi:10.1007/s10955-017-1836-5.

Special Topics Tutorials: code-oriented

Machine Learning Tutorial for Beginners (Kaggle) link

Data Science Workshops link

General Tutorials and MOOCs

Machine Learning crash course (Google) link

Learning from Data (MOOC sponsored by Caltech) link

Machine Learning with Andrew Ng (Coursera) link

Neural Networks and Deep Learning with Geoff Hinton (Coursera) link

Data Science and ML Resources (Data Science Central) link

Shafkat, I. (2018). Intuitively Understanding Convolutions for Deep Learning Exploring the strong visual hierarchies that makes them work. Towards Data Science Medium blog, June 1.

Applications to Biology and Reviews

Angermueller, C., Parnamaa, T., Parts, L., and Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12, 878. doi:10.15252/msb.20156651.

Jones, D.T. (2019). Setting the standards for machine learning in biology. Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-019-0176-5.

Packer J.S. et.al (2019). A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science, doi:10.1126/science.aax1971.

Tang, B., Pan, Z., Yin, K., and Khateeb, A. (2019). Recent Advances of Deep Learning in Bioinformatics and Computational Biology. Frontiers in Genetics, doi:10.3389/fgene.2019.00214.

Tarca, A.L., Carey, V.J., Chen, X-W., Romero, R., and Draghici, S. (2007) Machine Learning and Its Applications to Biology. PLoS Compututatiuonal Biology, 3(6), e116. doi:10.1371/journal.pcbi.0030116.

Xu, C. and Jackson, S.A. (2019). Machine learning and complex biological data. Genome Biology, 20, 76.

Developmental Generative Adversarial Networks (GANs):

Dirvanauskas, D., Maskeliunas, R., Raudonis, V., Damasevicius, R., and Scherer, R. (2019). HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks. Sensors, 19, 3578. doi:10.3390/s19163578.

Ghahramani, A., Watt, F.M., and Luscombe, N.M. (2018). Generative adversarial networks simulate gene expression and predict perturbations in single cells. bioRxiv, doi:10.1101/262501.

Han, L., Murphy, R.F., and Ramanan, D. (2018). Learning Generative Models of Tissue Organization with Supervised GANs. IEEE Winter Conference on Applied Compututer Vision, 682–690. doi:10.1109/WACV.2018.00080.

Huang, H., Zhoutao, W., Gong, Y., Xu, Q. (2018). Contour Extraction of Drosophila Embryos Based on Conditional Generative Adversarial Nets. 7th International Conference on Digital Home (ICDH). doi:10.1109/ICDH.2018.00022.

Tools and Tutorials

MetaCell Webinar: Intersections between Deep Learning and Neuroscience

Lineage-resolved Molecular Atlas (VisCello, single cell data explorer): link

"Machine Learning and Artificial Intelligence in Bioinformatics" section in BMC Bioinformatics link

Worm Neural Information Processing workshop (WNIP). NeurIPS Workshop, December 8, 2017. link

Allied Methods

Evolution Strategies as a Scalable Alternative to Reinforcement Learning (OpenAI) link

Samuel, A.L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 207-226 Fermat's Library

Edward Raff: A Step Toward Quantifying Independently Reproducible Machine Learning Research. [Github repo] (https://github.com/EdwardRaff/Quantifying-Independently-Reproducible-ML)

The Poisson Distribution and Poisson Process Explained link