• discriminative vs generative classifiers (http://ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf)
• solve logistic regression via iterated reweighed least square (http://www.win-vector.com/blog/2011/09/the-simpler-derivation-of-logistic-regression/)

• UCL RL David Silver (http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
  • DennyBritz (https://github.com/dennybritz/reinforcement-learning)
• bayesian ML McGill (http://www.cs.mcgill.ca/~dprecup/courses/ML/Lectures/)
• cs224 stanford NLP notes (http://web.stanford.edu/class/cs224n/syllabus.html)
• cs10si tensorflow tut (http://web.stanford.edu/class/cs20si/syllabus.html)
• David Duvenaud courses (http://www.cs.toronto.edu/~duvenaud/)

• nvidia digits object detection (https://github.com/NVIDIA/DIGITS/tree/master/examples/object-detection)
• Survey of text summarization (https://www.cs.cmu.edu/~afm/Home_files/Das_Martins_survey_summarization.pdf)
• chatbot iwth RL (https://marsan-ma.github.io/tensorflow-seq2seq-chatbot/)
• noisy channel model for spelling (https://sandipanweb.wordpress.com/2017/05/06/some-nlp-spelling-correction-with-noisy-channel-model/)
• Data augmentation with cyclegan (https://www.arxiv-vanity.com/papers/1711.00648v1/)
• Smart Augmentation Learning an Optimal Data Augmentation Strategy (https://arxiv.org/pdf/1703.08383.pdf)
• connectionist temporal classification for language recognition (https://distill.pub/2017/ctc/)
• distilling a NN into a soft decision tree (https://arxiv.org/pdf/1711.09784.pdf)
• leakGan for text generation (https://arxiv.org/pdf/1709.08624.pdf)
• dual-path convolutonal image-text embedding (https://arxiv.org/pdf/1711.05535.pdf)
• deep matching autoencoders (https://arxiv.org/pdf/1711.06047.pdf)
• time contrastive learning (https://arxiv.org/pdf/1704.06888.pdf)
• reinforcement learning cs231n (http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf)
• easy finance notebooks (https://github.com/yhilpisch/dx)
• TopicRNN (https://arxiv.org/abs/1611.01702)
• Cycle Consistent Adversarial Domain Adaption (https://arxiv.org/pdf/1711.03213v1.pdf)
• Lifeline - Survival analysis lib (https://lifelines.readthedocs.io/en/latest/)
• Modern OCR pipeline (https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/)
• Actor Critic Lunar Landing (https://github.com/FitMachineLearning/FitML)
• impl of Learning Deep Features for Discriminative Localization (https://github.com/jazzsaxmafia/Weakly_detector)
• chatbot tensorflow (https://github.com/DongjunLee/conversation-tensorflow)
• 3d GAN (https://github.com/robbiebarrat/Sculpture-GAN)
• altcoin prediction (https://github.com/SkyHenryk/altcoin_max_price_prediction)
• Object detection overview (https://www.saagie.com/fr/blog/object-detection-part1)
• starcraft II RL tut (http://chris-chris.ai/2017/08/30/pysc2-tutorial1/)
• impl neural vqa tensorflow (https://github.com/paarthneekhara/neural-vqa-tensorflow)
• polylingual topic model (http://www.ccs.neu.edu/home/dasmith/pltm.pdf)
• identifying fake instagram (https://srfdata.github.io/2017-10-instagram-influencers/)
• lattice regression (https://papers.nips.cc/paper/3694-lattice-regression.pdf)
• Bayesian Nonparametrics dirichlet (https://blog.statsbot.co/bayesian-nonparametrics-9f2ce7074b97)
• pixelCNN (https://arxiv.org/pdf/1706.00531.pdf)
• simple dqn / arcade games (https://github.com/tambetm/simple_dqn)
• deconv network (http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf)
• conv net facial detection (http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)
• neural style (https://arxiv.org/pdf/1508.06576.pdf)
• serving wide and deep net with tensorflow serving (https://github.com/MtDersvan/tf_playground/blob/master/wide_and_deep_tutorial/wide_and_deep_basic_serving.md)
• wide and deep learning for recommender systems (https://arxiv.org/pdf/1606.07792.pdf)
• tensorflow deep and wide network (https://github.com/ichuang/tflearn_wide_and_deep)
• non-parametric reg (https://nbviewer.jupyter.org/gist/fonnesbeck/2352771)
• tryout keras-rl (https://github.com/matthiasplappert/keras-rl)
• text summarization seq2seq (https://github.com/Currie32/Text-Summarization-with-Amazon-Reviews/blob/master/summarize_reviews.ipynb)
• semi-supervised sequence learning (https://arxiv.org/pdf/1511.01432.pdf)
• Recurrent Neural Networks for Noise Reduction in Robust ASR (http://www1.icsi.berkeley.edu/~vinyals/Files/rnn_denoise_2012.pdf)
• tensorboard tut (http://ischlag.github.io/2016/06/04/how-to-use-tensorboard/)
• R-CNN original paper (https://arxiv.org/pdf/1311.2524v5.pdf)
• VAE code generation (https://www.youtube.com/watch?v=czalwzb5FHY)
• benchmark for fake news dataset (https://arxiv.org/pdf/1705.00648.pdf)
• Face detection and bounding box aggregation (https://arxiv.org/pdf/1705.02402.pdf)
• neural machine translation in linear time (https://arxiv.org/pdf/1610.10099.pdf)
• axiomatic attribution for deep networks (https://arxiv.org/pdf/1703.01365.pdf)
• wavenet (https://deepmind.com/blog/wavenet-generative-model-raw-audio/)
• Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario (https://arxiv.org/pdf/1709.01584.pdf)
• Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm (https://arxiv.org/pdf/1708.00524.pdf)
• beginner's review of GAN architectures (https://sigmoidal.io/beginners-review-of-gan-architectures/)
• recent trends in deep learning and nlp (https://arxiv.org/pdf/1708.02709.pdf)
• robust continuous clustering (http://www.pnas.org/content/early/2017/08/28/1700770114.full.pdf)
• constructing 3d models CNN (https://arxiv.org/pdf/1704.00710.pdf)
• ICML field report (https://gmarti.gitlab.io/ml/2017/08/11/ICML-2017-field-reports.html)
• euler's relations between exponential, sine and cosine (http://www.mathcentre.ac.uk/resources/Engineering%20maths%20first%20aid%20kit/latexsource%20and%20diagrams/7_7.pdf)
• dft decomposition
• periodogram - identifying strong frequencies (simulation w/ simple TS)
• periodicity detection (http://www.l3s.de/~anand/tir14/lectures/ws14-tir-foundations-2.pdf)
• deep learning anomaly detection (https://docs.google.com/presentation/d/1HNeSZ0P2WQq0yx9xQXNRb9nkIkcykNUhJvMDwlpJbz4/edit#slide=id.p)
• active learning example (https://github.com/flowersteam/naminggamesal)
• learning to rank using gradient descent (http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)
• online learning adaptive learning rate (https://courses.cs.washington.edu/courses/cse599s/12sp/scribes/lecture_6.pdf)
• Learning to Generate Reviews and Discovering Sentiment (https://arxiv.org/pdf/1704.01444.pdf)
• pivot tables in excel (https://www.gcflearnfree.org/excel2016/intro-to-pivottables/1/)
• multinomial logistic regression (http://data.princeton.edu/wws509/notes/c6.pdf)
• bayesian interpretation of regularization (http://www.mit.edu/~9.520/spring09/Classes/class15-bayes.pdf)
• subgradient methods (https://www.cs.cmu.edu/~ggordon/10725-F12/slides/06-sg-method.pdf)
• do-rank (https://www.dropbox.com/s/sxg2s2cfh7aezi6/Do-Rank-DCG-Based-Machine-Leanring.pdf?dl=0)
• no R^2 for non-linear models (http://blog.minitab.com/blog/adventures-in-statistics-2/why-is-there-no-r-squared-for-nonlinear-regression)
• coordinate descent (https://engineering.jhu.edu/ams/wp-content/uploads/sites/44/2014/08/StephenWrightSlides112014.pdf)
• supervised random walk in social networks (http://cs.stanford.edu/people/jure/pubs/linkpred-wsdm11.pdf)
• loss functions (https://davidrosenberg.github.io/ml2015/docs/3a.loss-functions.pdf)
• minibatch metropolis-hastings (http://bair.berkeley.edu/blog/2017/08/02/minibatch-metropolis-hastings/)
• dl - rl in industry (https://drive.google.com/file/d/0BzUSSMdMszk6bEprTUpCaHRrQ28/view)
• dl - cnn review (https://drive.google.com/file/d/0B6NHiPcsmak1UHBYc0NxNkdGaE0/view)
• dl - automatic differentiation (https://drive.google.com/file/d/0B6NHiPcsmak1ckYxR2hmRGdzdFk/view)
• better communicating table values (https://www.displayr.com/the-magic-trick-that-highlights-significant-results-on-any-table/?utm_source=reddit&utm_medium=machine%20learning&utm_campaign=Trick%20that%20Highlights%20Results%20on%20Table)
• contextualized word vectors (https://einstein.ai/research/learned-in-translation-contextualized-word-vectors)
• svm dual coordinal descent (http://www.stat.ucdavis.edu/~chohsieh/teaching/ECS289G_Fall2015/lecture6.pdf)
• Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited (http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/255.pdf)
• primal soft margin svm - gradient descent impl (w261 11.8, constrained to unconstrained optimization, http://nbviewer.jupyter.org/urls/dl.dropbox.com/s/dm2l73iznde7y4f/SVM-Notebook-Linear-Kernel-2015-06-19.ipynb)
• distributed perceptron (http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36266.pdf)
• perceptron review (http://www.cs.cornell.edu/courses/cs678/2003sp/slides/perceptron_4up.pdf)
• boosting with logloss (http://web.mit.edu/marcoct/www/papers/boosting_log_loss.pdf)
• Assessing Retail Employee Risk Through Unsupervised Learning Techniques (https://arxiv.org/pdf/1707.04639.pdf)
• svm w/ RBF look at the alpha weights on the kernel
• image search via multiple color palettes (https://github.com/sergeyk/rayleigh)
• fourier transformation of TS data
• time series classification (http://didawikinf.di.unipi.it/lib/exe/fetch.php/dm/time_series_comparison_2012.pdf)
• clustering time series (http://www1.cs.columbia.edu/~jopa/Papers/PaparrizosSIGMOD2015.pdf)
• gaussian processes regression (http://www.gaussianprocess.org/gpml/chapters/RW2.pdf)
• exact logistic regression (http://www.cytel.com/hs-fs/hub/1670/file-2416929309-pdf/Pdf/Logistic-Regression---MEHTA-PATEL-Exact-Logistic-Regression-Theory-and-Examples-STATISTICS-IN-MEDICINE-1995.pdf)
• ensemble imbalanced class learning (https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/tsmcb09.pdf)
• label propagation graph semisupervised learning tutorial (http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf)
• convex optimization for machine learning (https://people.eecs.berkeley.edu/~jordan/courses/294-fall09/lectures/optimization/slides.pdf)
• LDA on graphs (https://arxiv.org/abs/1410.4510)
• machine learning techniques for BCI (http://doc.ml.tu-berlin.de/bbci/publications/MueKraDorCurBla04.pdf)
• multilingual embeddings (https://github.com/Babylonpartners/fastText_multilingual)
• contextual bandit langford tut (http://hunch.net/~exploration_learning/main.pdf)
• pinnability pinterest recommendations (https://medium.com/@Pinterest_Engineering/pinnability-machine-learning-in-the-home-feed-64be2074bf60)
• music generation / tensorfow tut (https://github.com/brannondorsey/midi-rnn)
• deepwalk (https://arxiv.org/pdf/1403.6652.pdf)
• diagonosing ML models (https://www.youtube.com/watch?v=0TSvo2hOKo0)
• MNIST PCA first 2 PC vis ESL pg537
• spectural clustering
• bayesian model averaging notes
• bayesian model averaging for regression (https://github.com/timsf/bma)
• kmedoids for strings (spelling correction)
• kMeans T = within + between
• mixed effect for panel data (https://arxiv.org/pdf/1406.5823.pdf)
• predicting similarity matrix via MF or regression
• semisupervised learning survey (http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf)
• density estimation via supervised learning (pg595 ESL)
• association rule tut (http://mhahsler.github.io/arules/)
• Silverman 1986 density estimation survey (https://ned.ipac.caltech.edu/level5/March02/Silverman/paper.pdf)
• lung cancer kaggle sol (https://eliasvansteenkiste.github.io/machine%20learning/lung-cancer-pred/)
• asymmetric gaussian (http://www.iic.ecei.tohoku.ac.jp/~kato/papers/t.kato_spr2002a.pdf)
• causal inference in online systems (http://blog.amitsharma.in/2016/06/27/a-gentle-introduction-to-causal-inference/)
• causal inference observational studies (http://www.cs.nyu.edu/~shalit/slides.pdf)
• kernel fisher LDA (http://www.ics.uci.edu/~welling/classnotes/papers_class/Fisher-LDA.pdf)
• discriminant adaptive nearest neighbors (http://www.cs.uvm.edu/~xwu/kdd/HT-KDD95.pdf)
• transformation invariance in pattern recognition (http://yann.lecun.com/exdb/publis/pdf/simard-00.pdf)
• SVD pseudo inverse proof (http://uspas.fnal.gov/materials/05UCB/6_SVD.pdf)
• SVD UCSD tut (http://mplab.ucsd.edu/wordpress/tutorials/svd.pdf)
• SVD computation (https://www.youtube.com/watch?v=cOUTpqlX-Xs)
• fuzzy SOM NN (http://www.cs.armstrong.edu/wsc11/slides/162.pdf)
• q-learning stock market (http://hallvardnydal.github.io/new_posts/2015-07-21-deep_q/)
• RL MDP simple tut (http://hunch.net/~jl/projects/RL/RLTheoryTutorial.pdf)
• q-learning tut
• figure out portfolio composition via optimization
• empirical bayesian techniques demo (http://varianceexplained.org/r/simulation-bayes-baseball/)
• one class collaborative filtering (http://www.rongpan.net/publications/pan-oneclasscf.pdf)
• NMF heatmap tut (http://nmf.r-forge.r-project.org/vignettes/heatmaps.pdf)
• metagenes and molecular pattern discovery using matrix factorization (http://www.pnas.org/content/101/12/4164.full.pdf)
• different weighting w/ covariate shift
• hinton diagrams on NMF embeddings
• topic modeling w/ NMF
• boltzmann machines for collaborative filtering (http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf)
• user-based CF, item-based CF - weighted KNN based on correlation
• recommendations content LR approach
• text summarisation text rank (http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf)
• MCMC and Applied Bayesian (http://www.stats.ox.ac.uk/~cholmes/Courses/BDA/bda_mcmc.pdf)
• bayesian technique parameter estimation (http://www4.ncsu.edu/~rsmith/MA797V_S12/MCMC.pdf)
• active portfolio management notes (https://github.com/RJT1990/Active-Portfolio-Management-Notes)
• graph centrality measures (degree, betweeness, closeness, eigenvector, katz, alpha)
• BoW + LSTM sentiment analysis (https://metamind.io/research/learning-when-to-skim-and-when-to-read)
• fb visdom tool (https://github.com/facebookresearch/visdom)
• topic coherence for LDA (http://qpleple.com/topic-coherence-to-evaluate-topic-models/)
• indexing by latent semantic analysis (http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf)
• topological data analysis (http://outlace.com/Topological+Data+Analysis+Tutorial+-+Part+1/)
• gensim summarization (https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/summarization_tutorial.ipynb)
• knn images from imagenet embeddings
• Forecasting: principles and practice chapter 7 to 9 (https://www.otexts.org/fpp)
• community detection via Girvan-Newman hierarhical clustering (https://github.com/riteshkasat/Community-Detection-Algorithm)
• LR vs LDA Efron paper (http://pegasus.cc.ucf.edu/~lni/sta6238/Efron1975Efficiency.pdf)
• isomap geodeisc distance
• multidimensional scaling tut (preserving interpoint dist)
• PCA gradient descent solver
• very sparse random projection (http://cseweb.ucsd.edu/~akmenon/VerySparseRPTalk.pdf)
• pyMix mixture models (http://www.pymix.org/pymix/)
• random walk bayesian NN (http://twiecki.github.io/blog/2017/03/14/random-walk-deep-net/)
• EM for data imputation (http://users.stat.umn.edu/~sandy/courses/8053/handouts/Missing.Data.Multiple.Imputation.pdf)
• locally optimized product quantization knn (http://image.ntua.gr/iva/files/lopq.pdf)
• survival analysis via weibull (http://www.stat.columbia.edu/~madigan/W2025/notes/survival.pdf)
• silhouette score review
• spherical k-means (cosine dist) (https://www.jstatsoft.org/article/view/v050i10/v50i10.pdf)
• credit card fraud readup
• recursive autoencoders (http://www.socher.org/index.php/Main/Semi-SupervisedRecursiveAutoencodersForPredictingSentimentDistributions)
• hyperparam tuning - automated machine learning (https://people.eecs.berkeley.edu/~kjamieson/hyperband.html)
• hyperband bandit param opt (https://people.eecs.berkeley.edu/~kjamieson/hyperband.html)
• huffman tree with frequency (https://www.siggraph.org/education/materials/HyperGraph/video/mpeg/mpegfaq/huffman_tutorial.html)
• dual form perceptron
• classifier comparison pitfalls (http://www.cs.bilkent.edu.tr/~guvenir/courses/CS553/On%20Comparing%20Classifiers%20Pitfalls%20to%20Avoid%20and%20a%20recommended%20approach.pdf)
• model assisted sampling (https://github.com/facebookincubator/ml_sampler/blob/master/ml_sampler.pdf)
• prophet forecast library test (https://research.fb.com/prophet-forecasting-at-scale/)
• surprise - bayesian weighting map (http://idl.cs.washington.edu/files/2017-SurpriseMaps-InfoVis.pdf)
• feature engineering notes (https://www.slideshare.net/HJvanVeen/feature-engineering-72376750)
• bayesian neural networks
• MCMC for sampling from posterior ESLR, pg279
• automatic relevance determination
• bass curve (nls w/ 3.12 pg52 IntroTimeSeries Cowpertwait)
• weight elimination (https://papers.nips.cc/paper/323-generalization-by-weight-elimination-with-application-to-forecasting.pdf)
• stochastic gradient boosting notes
• HOG (CV) (http://mccormickml.com/2013/05/09/hog-person-detector-tutorial/)
• ARCH / GARCH tutorial (http://www.quantatrisk.com/2014/10/23/garch11-model-in-python/)
• radial basis function network (RBFN) (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.109.312&rep=rep1&type=pdf)
• Gauss-Newton method for non-linear least squares (http://www.seas.ucla.edu/~vandenbe/103/lectures/nlls.pdf)
• sigmoid (W^T X) operates in the linear range if W^{norm} is very small demo
• ICA
• Missing At Random (MAR test) (http://stats.stackexchange.com/questions/11991/are-misses-in-my-data-distributed-completely-at-random)
• hierarchical mixture of experts (EM & interpretation)
• LDA notes (http://obphio.us/pdfs/lda_tutorial.pdf)
• STL notes (http://www.wessa.net/download/stl.pdf)
• poisson regression
• FTRL note (http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
• L-BFGS note
• collaborative filtering for ordinal scores (http://www.ijcai.org/Proceedings/13/Papers/449.pdf)
• stacking via CV pedictions (http://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html)
• stochastic search - bumping (XOR decision tree example)
• decision tree missing values (surrogate splits, 9.2.4 ELSL)
• isolation trees (http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf)
• factor analysis (http://web.stanford.edu/class/psych253/tutorials/FactorAnalysis.html)
• adaboost vs SVM (https://ucb-mids.s3.amazonaws.com/prod/Machine+Learning/Readings/Week+4/ShortIntroToBoosting.pdf)
• 1NN curse of dim test w/ b-v tradeoff (pg 24, 223)
• LOESS & show that boundary fit is linear
• splines in python, b-splines, thin plate spline
• do bfgs on linear and logistic regression
• linear discriminant analysis (fisher and gaussian derivations)
• quadratic discriminant analysis (https://www.youtube.com/watch?v=JWozRg_X-Vg)
• reduced-rank regression (canonical correlation analysis)
• compressed sensing (http://web.yonsei.ac.kr/nipi/lectureNote/Compressed%20Sensing%20by%20Romberg%20and%20Wakin.pdf)
• steepest descent (https://www.rose-hulman.edu/~bryan/lottamath/steepest.pdf)
• conjugate gradient (http://sep.stanford.edu/data/media/public/oldreports/sep44/44_14.pdf)
• gaussian processes test
• asymptotic normality of MLE (var 2nd deriv)
• gaussian processes for hyperparam optimization
• breaking news prediction on twitter
• multilingual spam filter

• Algorithmic marketing (https://algorithmicweb.files.wordpress.com/2017/11/algorithmic-marketing-r1-0-20171125.pdf)
• Deep RL Camp (https://sites.google.com/view/deep-rl-bootcamp/lectures)
• berkeley cs194 Russell (https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/)
• berkeley stat 153 (https://www.stat.berkeley.edu/~yuekai/153/)
• ensemble methods (http://www2.islab.ntua.gr/attachments/article/86/Ensemble%20methods%20-%20Zhou.pdf)
• online learning (https://courses.cs.washington.edu/courses/cse599s/14sp/scribes.html)
• stat learning theory (http://www.stat.cmu.edu/~ryantibs/statml/)
• cs224 stanford social network analysis (http://snap.stanford.edu/class/cs224w-2015/handouts.html)
• UofT cs441 notes (http://www.dgp.toronto.edu/~hertzman/411notes.pdf)
• graphical models (http://www.cs.cmu.edu/~epxing/Class/10708-17/lecture.html)
• fourier transform ee261
• convex optimization (http://www.stat.cmu.edu/~ryantibs/convexopt/)
• gensim notebooks (https://github.com/RaRe-Technologies/gensim/tree/develop/docs/notebooks)
• linear alge interactive book (http://immersivemath.com/ila/index.html)
• kalman filter book (https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python)
• bayesian book (http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
• Udacity notebooks (https://github.com/Ryosuke-Y)
• Udacity courses (https://classroom.udacity.com/me)
• deep learning Montreal summer school (https://mila.umontreal.ca/en/cours/deep-learning-summer-school-2017/slides/)

• Unsupervised Machine Translation Using Monolingual Corpora Only (https://arxiv.org/pdf/1711.00043.pdf)
• Neural Attention Model for Abstract Sentence Summarization (https://arxiv.org/pdf/1509.00685v2.pdf)
• a gentle introduction to blockchain (https://bitsonblocks.net/2015/09/09/a-gentle-introduction-to-blockchain-technology/)
• clustering of time series subsequences is meaningless (http://www.cs.ucr.edu/~eamonn/meaningless.pdf)
• unpaired image-to-image translation using cycle-consistent adversarial network (https://arxiv.org/pdf/1703.10593.pdf)
• multiple time series forecasting (http://mariofilho.com/how-to-predict-multiple-time-series-with-scikit-learn-with-sales-forecasting-example/)
• quantile vs expectile regression (https://www.slideshare.net/charthur/quantile-and-expectile-regression)
• Learning Deep Features for Discriminative Localization (https://arxiv.org/pdf/1512.04150.pdf)
• loss functions for predicted click-through rates in auctions for online advertising (http://vita.mcafee.cc/PDF/loss.pdf)
• semi supervised learning with EM (https://github.com/jmschrei/pomegranate/blob/master/tutorials/Tutorial_8_Semisupervised_Learning.ipynb)
• try pomegranete (https://github.com/jmschrei/pomegranate/tree/master/tutorials)
• impl bytenet tensorflow (https://github.com/paarthneekhara/byteNet-tensorflow)
• tensorflow char-rnn trump tweets (https://www.kaggle.com/benhamner/clinton-trump-tweets/data) (https://github.com/crazydonkey200/tensorflow-char-rnn)
• RL series (https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0)
• generating sentences by prototype (https://arxiv.org/pdf/1709.08878.pdf)
• Attention in NN (http://akosiorek.github.io/ml/2017/10/14/visual-attention.html)
• Elements of fashion style (http://ranjithakumar.net/resources/vaccaro-uist2016-fashion.pdf)
• Image completion w/ GAN (http://bamos.github.io/2016/08/09/deep-completion/)
• content based image retrieval via conv denoising autoencoder (https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
• Online Linear Langford (http://hunch.net/~mltf/online_linear.pdf)
• DCGAN (https://arxiv.org/pdf/1511.06434.pdf)
• Generative Adversarial Text to Image Synthesis (https://arxiv.org/pdf/1605.05396.pdf)
• Evaluation of Output Embeddings for Fine-Grained Image Classification (https://arxiv.org/pdf/1409.8403.pdf)
• Learning Deep Representations of Fine-Grained Visual Descriptions (https://arxiv.org/pdf/1605.05395.pdf)
• SimGAN for captcha (http://rickyhan.com/jekyll/update/2017/09/04/simgan-captcha.html)
• GAN original paper (https://arxiv.org/pdf/1406.2661v1.pdf)
• cs20i tensorflow seq2seq (http://web.stanford.edu/class/cs20si/lectures/slides_13.pdf)
• flappy bird RL (https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html)
• tensorflow java (http://fdahms.com/2017/03/05/tensorflow-serving-jvm-client/)
• tensorflow examples (https://github.com/aymericdamien/TensorFlow-Examples/tree/master/notebooks)
• quantile regression example (http://www.statsmodels.org/dev/examples/notebooks/generated/quantile_regression.html)
• notes on loss functions (https://tech.yandex.com/catboost/doc/dg/concepts/loss-functions-docpage/)
• prodigy - active learning kit (https://explosion.ai/blog/prodigy-annotation-tool-active-learning)
• one billion word benchmark (https://arxiv.org/pdf/1312.3005.pdf)
• BLEU score (http://www.aclweb.org/anthology/P02-1040.pdf)
• multilingual language processing from bytes (https://arxiv.org/pdf/1512.00103.pdf)
• effective approaches to attention-based neural machine translation (https://nlp.stanford.edu/pubs/emnlp15_attn.pdf)
• Vanishing Gradient Example (http://web.stanford.edu/class/cs224n/lectures/vanishing_grad_example.html)
• A convolutonal neural netwrok for modeling sentences (http://www.aclweb.org/anthology/P14-1062)
• conv net for sentence classification imp
• conv net for sentence classification (https://arxiv.org/pdf/1408.5882.pdf)
• label propagation with applications in NLP (https://www.slideshare.net/dav009/label-propagation-semisupervised-learning-with-applications-to-nlp)
• Attention is all you need (https://arxiv.org/pdf/1706.03762.pdf)
• autoregressive async temporal CNN for time series
• ranking with decision trees (http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/materials/Schamoni_boosteddecisiontrees.pdf)
• square vs huberized squared error loss
• gradient boosting review (http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf)
• PLANET decision tree on hadoop (http://www.vldb.org/pvldb/2/vldb09-537.pdf)
• visual proof that NN can compute any function (http://neuralnetworksanddeeplearning.com/chap4.html)
• tensor multiplication (https://docs.scipy.org/doc/numpy/reference/generated/numpy.tensordot.html)
• faster RCNN (https://arxiv.org/pdf/1506.01497.pdf)
• Revisiting Unreasonable Effectiveness of Data in Deep Learning Era (https://arxiv.org/pdf/1707.02968.pdf)
• time series similarity measures (https://arxiv.org/pdf/1401.3973.pdf)
• linear program python lib (http://benalexkeen.com/linear-programming-with-python-and-pulp/)
• kalman filter algorithm guide (http://web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman%20filter.pdf)
• multiclass / label algorithms (http://scikit-learn.org/stable/modules/multiclass.html)
• regression and causality (http://www.soderbom.net/metrix2/lec3.pdf)
• newton raphson (http://www.sosmath.com/calculus/diff/der07/der07.html)
• gensim LSIs
• chisquare feature selection math
• VAE notes (http://kvfrans.com/variational-autoencoders-explained/)
• take notes on elastic search image search (https://github.com/tuan3w/visual_search)
• graph based recommendation demo (https://medium.com/@Pinterest_Engineering/introducing-pixie-an-advanced-graph-based-recommendation-system-e7b4229b664b)
• bootstrap AB test CI (https://github.com/facebookincubator/bootstrapped)
• beta distribution
• networkX tut (http://snap.stanford.edu/class/cs224w-2011/nx_tutorial/nx_tutorial.pdf)
• LSA tutorial (http://www.engr.uvic.ca/~seng474/svd.pdf)
• panel data R intro (https://www.princeton.edu/~otorres/Panel101R.pdf)
• Simple but tough-to-beat baseline for sentence embedding (https://openreview.net/pdf?id=SyK00v5xx)
• svd matrix inversion
• svd to pca
• random forest variance formula (p*var + (1 - p)/beta *var)
• softmax gating network (https://people.cs.pitt.edu/~milos/courses/cs2750-Spring04/lectures/class22.pdf)
• coclustering methods for recommendations (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.6458&rep=rep1&type=pdf)
• Using asymmetric distributions to improve text classifier probability estimates (https://pdfs.semanticscholar.org/0ad0/d7431ca1b49617e6e5199c0ab5fcec18564f.pdf)
• probability calibration via bayesian binning (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410090/)
• histogram binning for probability calibration (http://cseweb.ucsd.edu/~elkan/calibrated.pdf)
• arimax python (http://robjhyndman.com/hyndsight/arimax/)
• Facing Imbalanced Data Recommendations for the Use of Performance Metrics (http://www.pitt.edu/~jeffcohn/skew/PID2829477.pdf)
• exploiting time for causal inference (https://dsaber.com/2017/04/02/time-keeps-on-slipping-exploiting-time-for-causal-inference-with-difference-in-differences-and-panel-methods/)
• eigenface (http://www.face-rec.org/algorithms/PCA/jcn.pdf)
• online covariance formula (http://rebcabin.github.io/blog/2013/01/22/covariance-matrices/)
• dataset shift in classification (http://iwann.ugr.es/2011/pdf/InvitedTalk-FHerrera-IWANN11.pdf)
• probability calibration
• Quick Look at SVM blog (https://generalabstractnonsense.com/2017/03/A-quick-look-at-Support-Vector-Machines/)
• temporal regression (decaying RSS)
• brier score for prob calibration (https://timvangelder.com/2015/05/18/brier-score-composition-a-mini-tutorial/)
• lda w/ vw example (http://mlwave.com/tutorial-online-lda-with-vowpal-wabbit/)
• pca principal component vis (w207 class 11 notebook)
• MARS (pyEarth)
• Ljung-Box portmanteau test (http://stat.wharton.upenn.edu/~steele/Courses/956/Resource/TestingNormality/LjungBox.pdf)
• cohen kappa (https://onlinecourses.science.psu.edu/stat509/node/162)
• qr factorization
• skipgram, neg sampling notes (https://arxiv.org/pdf/1310.4546.pdf)
• principal component regression
• apriori algorithm (https://github.com/asaini/Apriori)
• market basket analysis (https://github.com/amitkaps/machine-learning/blob/master/cf_mba/notebook/2.%20Market%20Basket%20Analysis.ipynb)
• classification performance measures (https://www.cs.cornell.edu/courses/cs578/2003fa/performance_measures.pdf)
• kmeans w/ categorical data (http://edu.cs.uni-magdeburg.de/EC/lehre/sommersemester-2013/wissenschaftliches-schreiben-in-der-informatik/publikationen-fuer-studentische-vortraege/kMeansMixedCatNum.pdf)
• partial least square
• ts backtesting (http://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/)
• DBSCAN
• collabrative filtering with temporal dynamics (http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/p89-koren.pdf)
• euclidean distance weighted by gain ratio, KNN variant
• vector quantization (image reconstruction)
• simpson's paradox (http://corysimon.github.io/articles/simpsons-paradox/)
• eigen decomposition tut
• scalable hierarchical clustering via Spark (http://users.eecs.northwestern.edu/~cji970/pub/cjinBigDataService2015.pdf)
• kernel regression
• deannoymization of netflix dataset (https://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf)
• bias-variance example w207_lec_1
• generating rules from decision tree (https://www.mimuw.edu.pl/~son/datamining/DM/6-rules.pdf)
• pandasql tut (https://github.com/yhat/pandasql)
• AIC and BIC for scree plot
• memory based learning (http://www.cs.cornell.edu/courses/cs578/2007fa/CS578_knn_lecture.pdf)
• predicting good probabilities with supervised learning (http://www.datascienceassn.org/sites/default/files/Predicting%20good%20probabilities%20with%20supervised%20learning.pdf)
• gmm classification
• hinton diagrams & for linear reg (http://tonysyu.github.io/mpltools/auto_examples/special/plot_hinton.html)
• levenshtein string clustering (http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances)
• stitchfix algorithm tour (http://algorithms-tour.stitchfix.com/)
• PRIM bump hunting
• logistic regression training on ratio and weights
• logistic regression covariance of coefficients
• pyFlux presentation (https://github.com/RJT1990/PyData2016-SanFrancisco/blob/master/presentation_final.pdf)
• NMF (how it enforces Non-negativity)
• OAO vs OAA (https://hal.archives-ouvertes.fr/inria-00103955/document)
• perceptron implementation
• quora question nlp tut (https://www.linkedin.com/pulse/duplicate-quora-question-abhishek-thakur)
• naive bayes spam filter
• kernel density classification & kernel smoothing with different local kernels
• recommendations MF, item kNN on latent space
• affinity propagation clustering (http://www.igi.tugraz.at/lehre/MLA/WS07/MLA_AffinityPropagation.pdf)
• pagerank impl (https://github.com/ashkonf/PageRank)
• pagerank math
• bayes optimal error rate (http://stats.stackexchange.com/questions/4949/calculating-the-error-of-bayes-classifier-analytically)
• decision tree imple
• ch15 notes Hal Daume III unsupervised learning (KMeans + PCA)
• permutation importance (decision tree)
• ranking item recommendations for a user from matrix factorization
• hierarchical clustering dendrogram analysis
• imputation in scikit-learn (http://scikit-learn.org/stable/auto_examples/missing_values.html#sphx-glr-auto-examples-missing-values-py)
• twitter sentiment vs stock markers (https://arxiv.org/pdf/1010.3003.pdf)
• FTRL math (https://courses.cs.washington.edu/courses/cse599s/12sp/scribes/Lecture8.pdf)
• granger causality time series (http://www-bcf.usc.edu/~liu32/cause.pdf)
• Surprising Usefulness of Autoencoders (http://rickyhan.com/jekyll/update/2017/09/14/autoencoders.html)
• neural style tensorflow (https://github.com/anishathalye/neural-style)

• udacity ud501 ML for Trading

===

• different types of FMs
• Spark AllReduce
• lessons from Quora ML
• notes like this