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Automated classification of supernovae (SNe) based on optical photometriclight curve information is essential in the upcoming era of wide-field timedomain surveys, such as the Legacy Survey of Space and Time (LSST) conducted bythe Rubin Observatory. Photometric classification can enable real-timeidentification of interesting events for extended multi-wavelength follow-up,as well as archival population studies. Here we present the complete sample of5,243 "SN-like" light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey(PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey interms of cadence, filters and depth, making this a useful training set for thecommunity. Using this dataset, we train a novel semi-supervised machinelearning algorithm to photometrically classify 2,315 new SN-like light curveswith host galaxy spectroscopic redshifts. Our algorithm consists of a randomforest supervised classification step and a novel unsupervised step in which weintroduce a recurrent autoencoder neural network (RAENN). Our final pipeline,dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc,II, IIn, SLSN-I). We find the highest accuracy rates for Type Ia SNe and SLSNeand the lowest for Type Ibc SNe. Our complete spectroscopically- andphotometrically-classified samples break down into: 62.0% Type Ia (1839objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% TypeIbc (291 objects), and 1.6% Type I SLSNe (54 objects). Finally, we discuss howthis algorithm can be modified for online LSST data streams.

SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae