Linear Digressions

Linear Digressions

Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.

Episodes

Hurricane Forecasting @

📆 2017-09-18 03:37 / 00:27:57


Finding Spy Planes with Machine Learning @

📆 2017-09-11 04:11 / 00:18:09


Data Provenance @

📆 2017-09-04 03:35 / 00:22:48


Adversarial Examples @

📆 2017-08-28 04:25 / 00:16:11


Jupyter Notebooks @

📆 2017-08-21 03:09 / 00:15:50


Curing Cancer with Machine Learning is Super Hard @

📆 2017-08-14 03:49 / 00:19:20


KL Divergence @

📆 2017-08-07 05:07 / 00:25:38


Sabermetrics @

📆 2017-07-31 03:15 / 00:25:48


What Data Scientists Can Learn from Software Engineers @

📆 2017-07-24 03:52 / 00:23:46


Software Engineering to Data Science @

📆 2017-07-17 04:36 / 00:19:05


Re-Release: Fighting Cholera with Data, 1854 @

📆 2017-07-10 02:19 / 00:12:04


Re-Release: Data Mining Enron @

📆 2017-07-02 19:53 / 00:32:16


Factorization Machines @

📆 2017-06-26 04:23 / 00:19:54


Anscombe's Quartet @

📆 2017-06-19 04:19 / 00:15:39


Traffic Metering Algorithms @

📆 2017-06-12 05:01 / 00:18:34


Page Rank @

📆 2017-06-05 03:46 / 00:19:58


Fractional Dimensions @

📆 2017-05-29 04:54 / 00:20:28


Things You Learn When Building Models for Big Data @

📆 2017-05-22 03:44 / 00:21:39


How to Find New Things to Learn @

📆 2017-05-15 03:49 / 00:17:54


Federated Learning @

📆 2017-05-08 03:50 / 00:14:03


Word2Vec @

📆 2017-05-01 04:17 / 00:17:59


Feature Processing for Text Analytics @

📆 2017-04-24 04:17 / 00:17:28


Education Analytics @

📆 2017-04-17 04:09 / 00:21:05


A Technical Deep Dive on Stanley, the First Self-Driving Car @

📆 2017-04-10 03:50 / 00:40:42


An Introduction to Stanley, the First Self-Driving Car @

📆 2017-04-03 03:34 / 00:13:07


Feature Importance @

📆 2017-03-27 03:53 / 00:20:15


Space Codes! @

📆 2017-03-20 03:50 / 00:23:56


Finding (and Studying) Wikipedia Trolls @

📆 2017-03-13 02:44 / 00:15:50


A Sprint Through What's New in Neural Networks @

📆 2017-03-06 04:27 / 00:16:56


Stein's Paradox @

📆 2017-02-27 03:51 / 00:27:02


Empirical Bayes @

📆 2017-02-20 04:30 / 00:18:57


Endogenous Variables and Measuring Protest Effectiveness @

📆 2017-02-13 04:31 / 00:16:28


Calibrated Models @

📆 2017-02-06 02:56 / 00:14:32


Rock the ROC Curve @

📆 2017-01-30 04:38 / 00:15:52


Ensemble Algorithms @

📆 2017-01-23 03:31 / 00:13:08


How to evaluate a translation: BLEU scores @

📆 2017-01-16 02:59 / 00:17:06


Zero Shot Translation @

📆 2017-01-09 04:20 / 00:25:32


Google Neural Machine Translation @

📆 2017-01-02 02:44 / 00:18:12




How to Lose at Kaggle @

📆 2016-12-12 05:28 / 00:17:16


Attacking Discrimination in Machine Learning @

📆 2016-12-05 04:38 / 00:23:20


Recurrent Neural Nets @

📆 2016-11-28 03:47 / 00:12:36


Stealing a PIN with signal processing and machine learning @

📆 2016-11-21 03:32 / 00:16:55


Neural Net Cryptography @

📆 2016-11-14 05:06 / 00:16:16


Deep Blue @

📆 2016-11-07 05:20 / 00:20:05


Organizing Google's Datasets @

📆 2016-10-31 03:17 / 00:15:00


Fighting Cancer with Data Science: Followup @

📆 2016-10-24 03:58 / 00:25:48


The 19-year-old determining the US election @

📆 2016-10-17 03:01 / 00:12:28


How to Steal a Model @

📆 2016-10-10 00:57 / 00:13:36


Regularization @

📆 2016-10-03 04:13 / 00:17:27


The Cold Start Problem @

📆 2016-09-26 04:24 / 00:15:37


Open Source Software for Data Science @

📆 2016-09-19 06:27 / 00:20:05


Scikit + Optimization = Scikit-Optimize @

📆 2016-09-12 03:54 / 00:15:41


Two Cultures: Machine Learning and Statistics @

📆 2016-09-05 03:50 / 00:17:29


Optimization Solutions @

📆 2016-08-29 04:01 / 00:20:07


Optimization Problems @

📆 2016-08-22 02:25 / 00:17:50


Multi-level modeling for understanding DEADLY RADIOACTIVE GAS @

📆 2016-08-15 03:49 / 00:23:34


How Polls Got Brexit "Wrong" @

📆 2016-08-08 03:37 / 00:15:14


Election Forecasting @

📆 2016-08-01 04:40 / 00:28:59


Machine Learning for Genomics @

📆 2016-07-25 04:14 / 00:20:22


Climate Modeling @

📆 2016-07-18 04:26 / 00:19:49


Reinforcement Learning Gone Wrong @

📆 2016-07-11 04:42 / 00:28:16


Reinforcement Learning for Artificial Intelligence @

📆 2016-07-03 20:28 / 00:18:30



How the sausage gets made @

📆 2016-06-20 04:25 / 00:29:13


SMOTE: makin' yourself some fake minority data @

📆 2016-06-13 05:06 / 00:14:37


Conjoint Analysis: like AB testing, but on steroids @

📆 2016-06-06 04:13 / 00:18:27


Traffic Metering Algorithms @

📆 2016-05-30 03:57 / 00:17:30


Um Detector 2: The Dynamic Time Warp @

📆 2016-05-23 04:05 / 00:14:00


Inside a Data Analysis: Fraud Hunting at Enron @

📆 2016-05-16 04:36 / 00:30:28


What's the biggest #bigdata? @

📆 2016-05-09 03:28 / 00:25:31


Data Contamination @

📆 2016-05-02 04:24 / 00:20:58


Model Interpretation (and Trust Issues) @

📆 2016-04-25 02:45 / 00:16:57


Updates! Political Science Fraud and AlphaGo @

📆 2016-04-18 04:48 / 00:31:43


Ecological Inference and Simpson's Paradox @

📆 2016-04-11 04:43 / 00:18:32


Discriminatory Algorithms @

📆 2016-04-04 04:30 / 00:15:21


Recommendation Engines and Privacy @

📆 2016-03-28 04:46 / 00:31:33



A Data Scientist's View of the Fight against Cancer @

📆 2016-03-14 04:26 / 00:19:08


Congress Bots and DeepDrumpf @

📆 2016-03-11 05:17 / 00:20:47


Multi - Armed Bandits @

📆 2016-03-07 03:44 / 00:11:29


Experiments and Messy, Tricky Causality @

📆 2016-03-04 04:54 / 00:16:59


Backpropagation @

📆 2016-02-29 04:58 / 00:12:21


Text Analysis on the State Of The Union @

📆 2016-02-26 04:51 / 00:22:22


Paradigms in Artificial Intelligence @

📆 2016-02-22 05:32 / 00:17:20


Survival Analysis @

📆 2016-02-19 04:44 / 00:15:21


Gravitational Waves @

📆 2016-02-15 03:46 / 00:20:26


The Turing Test @

📆 2016-02-12 05:11 / 00:15:15


Item Response Theory: how smart ARE you? @

📆 2016-02-08 04:37 / 00:11:46


Go! @

📆 2016-02-05 05:52 / 00:19:59


Great Social Networks in History @

📆 2016-02-01 05:22 / 00:12:42


How Much to Pay a Spy (and a lil' more auctions) @

📆 2016-01-29 06:36 / 00:16:59


Sold! Auctions (Part 2) @

📆 2016-01-25 03:58 / 00:17:27


Going Once, Going Twice: Auctions (Part 1) @

📆 2016-01-22 04:40 / 00:12:39


Chernoff Faces and Minard Maps @

📆 2016-01-18 04:38 / 00:15:11


t-SNE: Reduce Your Dimensions, Keep Your Clusters @

📆 2016-01-15 05:05 / 00:16:55


The [Expletive Deleted] Problem @

📆 2016-01-11 05:23 / 00:09:54


Unlabeled Supervised Learning--whaaa? @

📆 2016-01-08 04:26 / 00:12:35


Hacking Neural Nets @

📆 2016-01-05 03:56 / 00:15:28


Zipf's Law @

📆 2015-12-31 19:08 / 00:11:43


Indie Announcement @

📆 2015-12-30 16:57 / 00:01:19


Portrait Beauty @

📆 2015-12-27 14:34 / 00:11:44


The Cocktail Party Problem @

📆 2015-12-18 01:17 / 00:12:04


A Criminally Short Introduction to Semi Supervised Learning @

📆 2015-12-04 04:13 / 00:09:12


Thresholdout: Down with Overfitting @

📆 2015-11-27 18:55 / 00:15:52


The State of Data Science @

📆 2015-11-10 05:36 / 00:15:40


Data Science for Making the World a Better Place @

📆 2015-11-06 04:43 / 00:09:31


Kalman Runners @

📆 2015-10-29 04:10 / 00:14:42


Neural Net Inception @

📆 2015-10-23 04:25 / 00:15:19


Benford's Law @

📆 2015-10-16 05:30 / 00:17:42


Guinness @

📆 2015-10-07 05:30 / 00:14:43


PFun with P Values @

📆 2015-09-02 05:24 / 00:17:07


Watson @

📆 2015-08-25 04:26 / 00:15:36


Bayesian Psychics @

📆 2015-08-18 02:05 / 00:11:44


Troll Detection @

📆 2015-08-07 22:56 / 00:12:57


Yiddish Translation @

📆 2015-08-03 05:06 / 00:12:15


Modeling Particles in Atomic Bombs @

📆 2015-07-07 01:30 / 00:15:38


Random Number Generation @

📆 2015-06-19 20:49 / 00:10:26


Electoral Insights (Part 2) @

📆 2015-06-09 04:46 / 00:21:18


Electoral Insights (Part 1) @

📆 2015-06-05 22:38 / 00:09:17


Falsifying Data @

📆 2015-06-01 23:04 / 00:17:46


Reporter Bot @

📆 2015-05-21 01:16 / 00:11:15


Careers in Data Science @

📆 2015-05-16 07:43 / 00:16:35


That's "Dr Katie" to You @

📆 2015-05-14 19:37 / 00:03:01


Neural Nets (Part 2) @

📆 2015-05-11 16:37 / 00:10:55


Neural Nets (Part 1) @

📆 2015-05-01 20:59 / 00:09:00


Inferring Authorship (Part 2) @

📆 2015-04-28 18:56 / 00:14:04


Inferring Authorship (Part 1) @

📆 2015-04-16 19:25 / 00:08:51


Statistical Mistakes and the Challenger Disaster @

📆 2015-04-06 21:36 / 00:13:09


Genetics and Um Detection (HMM Part 2) @

📆 2015-03-25 18:29 / 00:14:49


Introducing Hidden Markov Models (HMM Part 1) @

📆 2015-03-24 16:57 / 00:14:54


Monte Carlo For Physicists @

📆 2015-03-13 00:18 / 00:08:13


Random Kanye @

📆 2015-03-05 00:04 / 00:08:44


Lie Detectors @

📆 2015-02-25 19:20 / 00:09:17


The Enron Dataset @

📆 2015-02-09 01:00 / 00:12:27


Labels and Where To Find Them @

📆 2015-02-04 03:30 / 00:13:15


Um Detector 1 @

📆 2015-01-23 21:16 / 00:13:19


Better Facial Recognition with Fisherfaces @

📆 2015-01-07 02:33 / 00:11:56


Facial Recognition with Eigenfaces @

📆 2015-01-07 02:30 / 00:10:01


Stats of World Series Streaks @

📆 2014-12-17 01:41 / 00:12:34


Computers Try to Tell Jokes @

📆 2014-11-26 19:59 / 00:09:08


How Outliers Helped Defeat Cholera @

📆 2014-11-22 01:00 / 00:10:54


Hunting for the Higgs @

📆 2014-11-16 01:00 / 00:10:16