The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas:
- Discriminative models
- Generative models
- Reinforcement learning models
In particular we will cover the following:
- Linear Regression
- Logistic Regression
- Support Vector Machines
- Deep Nets
- Structured Methods
- Learning Theory
- kMeans
- Gaussian Mixtures
- Expectation Maximization
- Markov Decision Processes
- Q-Learning
Probability, Linear Algebra, and proficiency in Python.
- Machine Learning: A Probabilistic Perspective by Kevin Murphy
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Pattern Recognition and Machine Learning by Christopher Bishop
- Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman
- Assignment 1: Introduction + Python — Design by Colin, Review by Yucheng
- Assignment 2: Linear Regression — Design by Raymond, Review by Jyoti
- Assignment 3: Binary Classification — Design by Youjie, Review by Jyoti
- Assignment 4: Support Vector Machine — Design by Raymond, Review by Ishan
- Assignment 5: Multiclass Classification — Design by Yucheng, Review by Safa
- Assignment 6: Deep Neural Networks — Design by Safa, Review by Yuan-Ting
- Assignment 7: Structured Prediction — Design by Colin, Review by Yucheng
- Assignment 8: k-Means — Design by Jyoti, Review by Youjie
- Assignment 9: Gaussian Mixture Models — Design by Ishan, Review by Colin
- Assignment 10: Variational Autoencoder — Design by Yuan-Ting, Review by Raymond
- Assignment 11: Generative Adverserial Network — Design by Ishan, Review by Yuan-Ting
- Assignment 12: Q-learning — Design by Safa, Review by Youjie
All copyrights reserved © CS446 Instructors & TAs
- Raymond Yeh, Website [Link]
- Colin Graber
- Safa Messaoud
- Yuan Ting Hu
- Ishan Deshpande
- Jyoti Aneja
- Youjie Li
- Yucheng Chen
