as the continuous limit of many discretized algorithms, pdes can provide a qualitative description of algorithm’s behavior and give principled theoretical insight into many mysteries in machine learning. in this talk, i will give a theoretical interpretation of several machine learning algorithms using fokker-planck (fp) equations. in the first one, we provide a mathematically rigorous explanation of why resampling outperforms reweighting in correcting biased data when stochastic gradient-type algorithms are used in training. in the second one, we propose a new method to alleviate the double sampling problem in model-free reinforcement learning, where the fp equation is used to do error analysis for the algorithm. in the last one, inspired by an interactive particle system whose mean-field limit is a non-linear fp equation, we develop an efficient gradient-free method that finds the global minimum exponentially fast.