# COMS30035 Machine Learning Labs

| Lab | Labsheet | Answers |
|-----|----------|---------|
| 1   | [Introduction to numpy and scikit learn](lab1/lab1-intro_to_numpy_scikitlearn.ipynb) | [Link](lab1/lab1-intro_to_numpy_scikitlearn_answers.ipynb) |
| 2   | [Neural networks](lab2/lab2-neural_nets.ipynb) | [Link](lab2/lab2-neural_nets_answers.ipynb) |
| 3   | [Trees and SVMs](lab3/lab3-trees_SVM.ipynb) | [Link](lab3/lab3-trees_SVM_answers.ipynb) |
| 4   | [Probabilistic graphical models](lab4/lab4-Bayesian_Networks_MCMC_PyMC3.ipynb) | [Link](lab4/lab4-Bayesian_Networks_MCMC_PyMC3_answers.ipynb) |
| 5   | [Mixture models, K-means and Expectation Maximisation](lab5/lab5-MixtureModels_K-means_Expectation-Maximisation.ipynb) | [Link](lab5/lab5-MixtureModels_K-means_Expectation-Maximisation_answers.ipynb) |
| 6   | [Hidden Markov Models](lab6/Lab_6_HMM.ipynb) | [Link](lab6/Lab_6_HMM_answers.ipynb) |
| 7   | [Decision Trees and Ensemble Methods](lab7/lab7-trees_and_ensembles.ipynb) | [Link](lab7/lab7-trees_and_ensembles_answers.ipynb) |


Collaborative Calculation and Data Science

colby

sagemath

COMS30035 Machine Learning Labs

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Lab	Labsheet	Answers
1	Introduction to numpy and scikit learn	Link
2	Neural networks	Link
3	Trees and SVMs	Link
4	Probabilistic graphical models	Link
5	Mixture models, K-means and Expectation Maximisation	Link
6	Hidden Markov Models	Link
7	Decision Trees and Ensemble Methods	Link