lishaowen0426 / ml

Here is the list of what I have done in this course, the link points to a report which basically includes everything.

Stuff written by me are in color blue.

Actual implementations are under the code directory of every assignment.

a0: https://github.com/lishaowen0426/ml/blob/master/a0/doc/cpsc-340-assignment.pdf

Topic:

test of basic math knowledge: linear algebra, matrix operations, multivariable derivatives, basic data structure

a1: https://github.com/lishaowen0426/ml/blob/master/a1/doc/cpsc-340-a1.pdf

Topic:

data exploration(get statistics, data visualization) decision tree, knn, condensed nearest neighbours

a2: https://github.com/lishaowen0426/ml/blob/master/a2/doc/a2.pdf

Topic:

naive Bayes, Laplace smoothing, random forest, clustering/density based clustering, vector quantization

a3: https://github.com/lishaowen0426/ml/tree/master/a3/doc/a3.pdf

Topic:

robust regression, gradient descent, linear regrssion and nonliner bases, non-parametric bases, cross-validation

a4: https://github.com/lishaowen0426/ml/blob/master/a4/doc/a4.pdf

Topic:

convex function, logistic regressioin with L2/L1/L0 Regularization, multi-class logistic(one-vs-all,softmax), running cost

a5: https://github.com/lishaowen0426/ml/blob/master/a5/doc/a5.pdf

Topic:

MAP estimation, PCA and its application in data visualization,data compression, Robust PCA,multi-dimensional scaling, ISOMAP

a6: https://github.com/lishaowen0426/ml/blob/master/a6/doc/a6.pdf

Topic:

open-end mini project for application of all learned including basic use of neural network