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[Naive Bayes Notebook](NAIVE_BAYES/NaiveBayes_notebook.ipynb)\n  - [Naive Bayes Summary](NAIVE_BAYES/README.md)\n- [Linear Discriminant Analysis (LDA)](LDA)\n  - [LDA Notebook](LDA/LDA.ipynb)\n  - [LDA Summary](LDA/README.md)\n- [Perceptron](PERCEPTRON)\n  - [Perceptron Notebook](PERCEPTRON/Perceptron.ipynb)\n  - [Perceptron Summary](PERCEPTRON/README.md)\n- [Gradient Descent](GRADIENT_DESCENT)\n  - [Gradient Descent Notebook](GRADIENT_DESCENT/Gradient_Descent.ipynb)\n  - [Gradient Descent Summary](GRADIENT_DESCENT/README.md)\n- [Linear Regression](LINEAR_REGRESSION)\n  - [Linear Regression Notebook](LINEAR_REGRESSION/Linear_Regression.ipynb)\n  - [Lineas Regression Summary](LINEAR_REGRESSION/README.md)\n- [Support Vector Machine (SVM)](SVM)\n  - [Kernels Notebook](SVM/Kernels.ipynb)\n  - [SVM Summary](SVM/README.md)\n- [Neural Network](NEURAL_NETWORK)\n  - [Neural Network Notebook](NEURAL_NETWORK/Neural_Network.ipynb)\n  - [Neural Network Summary](NEURAL_NETWORK/README.md)\n- [Random Forest](RANDOM_FOREST)\n  - [Random Forest Notebook](RANDOM_FOREST/Random_Forest.ipynb)\n  - [Random Forest Summary](RANDOM_FOREST/README.md)\n- [Clustering](CLUSTERING)\n  - [Optics Notebook](CLUSTERING/Optics.ipynb)\n  - [Optics Summary](CLUSTERING/README.md)\n- [Reinforcement Learning](REINFORCEMENT_LEARNING)\n  - [Centipede Reinforcement Learning Notebook](REINFORCEMENT_LEARNING/Centipede_RL.ipynb)\n  - [Reinforcement Learning Summary](REINFORCEMENT_LEARNING/README.md)\n  \n\u003c/br\u003e\n\n## Course objective\n\n\"Gain an in-depth understanding of some of the major machine learning techniques: its algorithms, theory and application. In the same way, that he becomes familiar, through practice, with the procedure of elaboration of a model.\"\n\n-[Salvador Marmol](https://github.com/salvadormarmol), Machine Learning course professor\n\n\u003c/br\u003e\n\n## Course Syllabus\n\n- Machine Learning concepts\n- Supervised learning\n  - Basic Bayes method\n  - K-Nearest neighbors\n  - Linear regression\n  - Neural network\n  - Support vector machine\n  - Decision tree\n- Models evaluation and learning theory\n- Unsupervised learning\n  - A-priori algorithm\n  - K-means clustering\n  - Hierarchical clustering\n  - Density-Based clustering\n  - Dimensionality reduction method\n    - PCA\n    - T-SNE\n- Recomendation system\n- Model Assemblies\n  - Random forest\n  - Bagging\n  - Boosting\n- Deep Learning\n  - Convolutional neural network\n- Reinforcement Learning \n  - Deep reinforcement learning\n\n\u003c/br\u003e\n\n## Bibliography\n\n- Murphy, K. (2022) Probabilistic Machine Learning: An Introduction. Cambridge, MA: MIT.\n- Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer Series in Statistics, 2nd edition.\n- Bishop, C. M. (2006) Pattern Recognition and Machine Learning, New York, N. Y.: Springer Science + Business Media. \n- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. (2017) Deep Learning. Cambridge, MA: MIT.\n- Sutton, Richard S., and Andrew G. Barto. (2018) Reinforcement Learning: An Introduction. Cambridge, MA: MIT, 2nd edition.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmauriciovazquezm%2Fmachinelearning_course_spring2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmauriciovazquezm%2Fmachinelearning_course_spring2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmauriciovazquezm%2Fmachinelearning_course_spring2023/lists"}