vrdmr / CS273a-Introduction-to-Machine-LearningLinks
Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, o…
☆41Updated 10 years ago
Alternatives and similar repositories for CS273a-Introduction-to-Machine-Learning
Users that are interested in CS273a-Introduction-to-Machine-Learning are comparing it to the libraries listed below
Sorting:
- machine learning☆39Updated 6 years ago
- Parametric Gaussian Process Regression for Big Data (Matlab Version)☆24Updated 7 years ago
- Topics - Linear Regression, Logistic Regression, Regularization, Neural Networks, System Design, Support Vector Machines, Unsupervised Le…☆24Updated 6 years ago
- Artificial Neural Networks - Gradient descent, BFGS, Regularization with Jupyter notebook☆65Updated 3 years ago
- YAGTOM: Yet Another Guide TO Matlab☆56Updated 11 years ago
- Deep Learning application to the partial differential equations☆30Updated 7 years ago
- Symbolic computation using SymPy and various applications☆21Updated 6 years ago
- Heuristic Optimization for Python☆72Updated 5 years ago
- Python demos for Chris Bishop's PRML textbook, and other machine learning stuff☆23Updated 3 years ago
- ☆40Updated 8 years ago
- UNIVR PDE course project and just for fun☆117Updated 8 years ago
- Matlab implementation of Machine Learning algorithms☆59Updated 10 years ago
- Feature reduction using genetic algorithm☆26Updated 2 years ago
- A MATLAB implementation of the TensorFlow Neural Networks Playground seen on http://playground.tensorflow.org/☆72Updated 8 years ago
- zeta-lean: minimalistic python machine learning library built on top of numpy and matplotlib☆39Updated 4 years ago
- Python Implementation of k-means clustering☆26Updated 3 years ago
- Advanced Machine Learning and Signal Processing IBM☆17Updated 5 years ago
- Stanford machine learning class on Coursera. Taught by Andrew Ng. Implemented the assignments with Matlab.☆24Updated 8 years ago
- Slides and lecture notes for the course 'machine learning I' taught at the Graduate School Neural Information Processing in Tuebingen.☆30Updated 9 years ago
- A python metaheuristic optimization library. Currently supports Genetic Algorithms, Gravitational Search, Cross Entropy, and PBIL.☆39Updated 4 years ago
- The effects of sparse and group-feature regression models in portfolio optimization.☆24Updated 11 years ago
- A Python Package for data processing and building ML models, primarily based on pandas and sklearn libraries.☆17Updated 5 years ago
- A few basic online learning algorithms☆24Updated 3 years ago
- Random Forest Library In Python Compatible with Scikit-Learn☆14Updated 3 years ago
- Code for determining optimal number of clusters for K-means algorithm using the 'elbow criterion'☆42Updated 2 weeks ago
- Course material for the PhD course Advanced Bayesian Learning at Linköping University☆14Updated 10 years ago
- Matlab implementation of the EM and MCMC algorithm for SVMs as introduced in the paper "Data augmentation for support vector machines"☆18Updated 10 years ago
- Data Analysis and Machine Learning with Python: EDA with ECDF and Correlation analysis, Preprocessing and Feature engineering, L1 (Lasso)…☆32Updated 7 years ago
- Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)☆55Updated 6 years ago
- Code for Kaggle's Default Loan Prediction - Imperial College London challenge.☆29Updated 11 years ago