https://github.com/mauriciovazquezm/machinelearning_course_spring2023

Machine Learning course tasks focused on the implementation of the ML algorithms using libraries such as Numpy, Pandas, etc.
https://github.com/mauriciovazquezm/machinelearning_course_spring2023

machine-learning machine-learning-algorithms numpy python r-language

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Machine Learning course tasks focused on the implementation of the ML algorithms using libraries such as Numpy, Pandas, etc.

• Host: GitHub
• URL: https://github.com/mauriciovazquezm/machinelearning_course_spring2023
• Owner: MauricioVazquezM
• Created: 2023-06-12T19:34:10.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-19T03:01:29.000Z (5 months ago)
• Last Synced: 2024-06-19T10:30:45.628Z (5 months ago)
• Topics: machine-learning, machine-learning-algorithms, numpy, python, r-language
• Language: Jupyter Notebook
• Homepage:
• Size: 16.1 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# MachineLearning_Course_Spring2023







## Team

- [Guillermo Arredondo](https://github.com/guillermoArr), student of a double BS degree in Data Science and Applied Mathematics at ITAM.

- [Iñaki Fernandez](https://github.com/Inaki-FF), student of a BS degree in Data Science at ITAM.

- [Mauricio Vazquez](https://github.com/MauricioVazquezM), student of a double BS degree in Data Science and Actuarial Science at ITAM.

## Repository Content

- [Naive Bayes Algorithm Implementation](NAIVE_BAYES)

  - [Naive Bayes Notebook](NAIVE_BAYES/NaiveBayes_notebook.ipynb)

  - [Naive Bayes Summary](NAIVE_BAYES/README.md)

- [Linear Discriminant Analysis (LDA)](LDA)

  - [LDA Notebook](LDA/LDA.ipynb)

  - [LDA Summary](LDA/README.md)

- [Perceptron](PERCEPTRON)

  - [Perceptron Notebook](PERCEPTRON/Perceptron.ipynb)

  - [Perceptron Summary](PERCEPTRON/README.md)

- [Gradient Descent](GRADIENT_DESCENT)

  - [Gradient Descent Notebook](GRADIENT_DESCENT/Gradient_Descent.ipynb)

  - [Gradient Descent Summary](GRADIENT_DESCENT/README.md)

- [Linear Regression](LINEAR_REGRESSION)

  - [Linear Regression Notebook](LINEAR_REGRESSION/Linear_Regression.ipynb)

  - [Lineas Regression Summary](LINEAR_REGRESSION/README.md)

- [Support Vector Machine (SVM)](SVM)

  - [Kernels Notebook](SVM/Kernels.ipynb)

  - [SVM Summary](SVM/README.md)

- [Neural Network](NEURAL_NETWORK)

  - [Neural Network Notebook](NEURAL_NETWORK/Neural_Network.ipynb)

  - [Neural Network Summary](NEURAL_NETWORK/README.md)

- [Random Forest](RANDOM_FOREST)

  - [Random Forest Notebook](RANDOM_FOREST/Random_Forest.ipynb)

  - [Random Forest Summary](RANDOM_FOREST/README.md)

- [Clustering](CLUSTERING)

  - [Optics Notebook](CLUSTERING/Optics.ipynb)

  - [Optics Summary](CLUSTERING/README.md)

- [Reinforcement Learning](REINFORCEMENT_LEARNING)

  - [Centipede Reinforcement Learning Notebook](REINFORCEMENT_LEARNING/Centipede_RL.ipynb)

  - [Reinforcement Learning Summary](REINFORCEMENT_LEARNING/README.md)

  

## Course objective

"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."

-[Salvador Marmol](https://github.com/salvadormarmol), Machine Learning course professor

## Course Syllabus

- Machine Learning concepts

- Supervised learning

  - Basic Bayes method

  - K-Nearest neighbors

  - Linear regression

  - Neural network

  - Support vector machine

  - Decision tree

- Models evaluation and learning theory

- Unsupervised learning

  - A-priori algorithm

  - K-means clustering

  - Hierarchical clustering

  - Density-Based clustering

  - Dimensionality reduction method

    - PCA

    - T-SNE

- Recomendation system

- Model Assemblies

  - Random forest

  - Bagging

  - Boosting

- Deep Learning

  - Convolutional neural network

- Reinforcement Learning 

  - Deep reinforcement learning

## Bibliography

- Murphy, K. (2022) Probabilistic Machine Learning: An Introduction. Cambridge, MA: MIT.

- Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer Series in Statistics, 2nd edition.

- Bishop, C. M. (2006) Pattern Recognition and Machine Learning, New York, N. Y.: Springer Science + Business Media. 

- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. (2017) Deep Learning. Cambridge, MA: MIT.

- Sutton, Richard S., and Andrew G. Barto. (2018) Reinforcement Learning: An Introduction. Cambridge, MA: MIT, 2nd edition.