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master_alg_c

matrix_oo

Object-oriented matrix implementation in C99

maxentropy

Maximum entropy and minimum divergence models in Python

ml-road

Machine Learning Resources, Practice and Research

mlapp

Machine Learning: a Probabilistic Perspective

mlapp-cn

A Chinese Notes of MLAPP，MLAPP 中文笔记项目 https://zhuanlan.zhihu.com/python-kivy

my-notes

mydl

mylearn

machine learning algorithm

mytinystl

Achieve a tiny STL in C++11

nlp

NLP_CWS

numpy

The fundamental package for scientific computing with Python.

ocp

Optimal control problem solver

omegaui

Backtrader Front End

onepy

Python Backtesting library for OnePiece in trading.

onlinelowrank

Source code for the AAAI 2020 paper "Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise" (https://arxiv.org/abs/1809.03550)

opentrade

An open source OEMS, and algorithmic trading platform in modern C++

or-tools

Google's Operations Research tools

pair-trading

Pair trading in C++

pairs-trading-cpp

Pairs Trading Strategy Implementation in C++

parallelsparsematrixfactorization

Sparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.

paristradingsystem

a pairs trading system prototype in C++

phd-bibliography

References on Optimal Control, Reinforcement Learning and Motion Planning

pipe

A simple thread-safe FIFO in C.

portfolio.optimization

:exclamation: This is a read-only mirror of the CRAN R package repository. portfolio.optimization — Contemporary Portfolio Optimization. Homepage: http://www.finance-r.com/

prism

a strategy trading backtester, quant, backtesting.

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming in data analysis with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

prophet

Financial markets analysis framework for programmers

psgexpress

:exclamation: This is a read-only mirror of the CRAN R package repository. PSGExpress — Portfolio Safeguard: Optimization, Statistics and Risk Management. Homepage: http://www.aorda.com

pslrhmm

Parallel sparse left-right hidden markov model c++ library.