Theory of (Machine) Learing
Instructors: Mohsen Rezapour, Hossein Jowhari
Semester: Fall 2018 (97-1)
Time: SUN, TUE 1:30pm-3:00pm
Textbook: Learning from Data . Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin. 2012
Lectures :
- Introduction to Machine Learning.
- Perceptron algorithm. Proof of convergence.
- A brief guide to Python.
- Implementing the Perceptron in Python.
- Feasibility of learning: Hoeffding Model
- Theory of generalization (VC-dimension, growth function)
- Proof of the VC bound
- Interpretting the VC bound
- Wrapping up: Testing vs Training
- Classification: Support Vector Machines
- Classification: Soft Margin and Kernels
- Classification: Nearest Neighbor Search
- Linear Regression
- Logistic Regression I
- Logistic Regression II
- Validation and Model Selection
- Overfitting
- Bias and Variance, Regularization
- Regression and Regularization: Implementation in Python
- Unsupervised Learning, Clustering
- Clustering
Homeworks :
Useful Links :
A Guide for matplotlib
A book on data science using python
Sample python code for SVM
Sample python code for linear regression