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