Machine Learning projects without the use of any ML libraries.
Projects in the order of time (old -> recent):
- kNN:
- k-Nearest Neighbours method used on both and classification
- Includes a comparison with linear regression
- Includes a comparison of different methods to improve time-efficiency
- GLM:
- Generalized Linear Model
- Used different generalized functions and their corresponding kernels to fit the Mauna_loa dataset
- Compared the time-efficiency using primary vs. dual representation
- GD-SGD:
- Playing around with different configurations of Gradient Descent and Stochastic GD
- NN:
- Built a 2-layer neural network from scratch to train on the Mnist_small dataset
- Visualized the most heavily-weighed parameters in the first hidden layer
- Compared the accuracy of training vs. validation set (i.e. learning curve)
- Observed the ones that are "not confident" vs. those that are predicted incorrectly
- Bayesian:
- Implemented Bayesian inference with various approximation methods including Laplace and MCMC Sampling
- For each method, compared different configurations
Please see the .ipynb files for the code and demo. Each project also has a report in the report folder.