Machine Learning
1.[ML] 1. Linear Regression
Lecture Week 1,2. Linear Regression with One Variable and Multiple Variables 1. Supervised learning : infers a function from labeled training data 1
2.[ML] 2. Logistic Regression for Classification
Logistic Regression: a classification algorithm, used when the value of the target variable is categorical in nature. used when the data has binary ou
3.[ML] 3. Neural Networks
Neuron unitactivation functions(a) step/threshold function, (b) sigmoid function $\\frac{1}{1 + e^{-x}}$Changing the bias weight $W\_{0,i}$ moves the
4.[ML] 5. Deep Learning (CNNs)
Lecture Week 7-1. Deep Learning (CNNs) background
5.[ML] 6. Support Vector Machine (SVM)
Week 10-1. SVM (Support Vector Machine) Theory and Intuition: Hyperplanes and Margins Hyperplane : a flat subspace (N-1 dimension) that divides a hi
6.[ML] 7. Principal Component Analysis (PCA)
dimension reductionto understand key features, the most variance in data set (high variance high importance)visualization, data analysiscreate new dim
7.[ML] 8. Naive Bayes (NB)
Week 12. Naive Bayes Supervised Learning text tasks, classification Naive Bayes and NLP (use raw string text) 1. Bayes' Theorem $\displaystyle P(A|B)
8.[ML] 9. Unsupervised Learning: Clustering
Clustering Methods•Hard clustering (Crisp clustering)Create non-overlapping clusters Each object is assigned to only one cluster• Soft clustering (Fuz