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machine-learning-uiuc's Introduction

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Table of Contents:

Course Information:

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:

  1. Discriminative models
  2. Generative models
  3. 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

Pre-requisites:

Probability, Linear Algebra, and proficiency in Python.

Recommended Text:

  1. Machine Learning: A Probabilistic Perspective by Kevin Murphy
  2. Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
  3. Pattern Recognition and Machine Learning by Christopher Bishop
  4. Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman

Instructors:

  • Alexander Schwing, Website [Link]
  • Matus Telgarsky, Website [Link]

Assignments

  • Assignment 1: Introduction + Python โ€” Design by Colin, Review by Yucheng
  • Assignment 2: Linear Regression โ€” Design by Raymond, Review by Jyoti
  • Assignment 3: Binary Classification โ€” Design by Youjie, Review by Jyoti
  • Assignment 4: Support Vector Machine โ€” Design by Raymond, Review by Ishan
  • Assignment 5: Multiclass Classification โ€” Design by Yucheng, Review by Safa
  • Assignment 6: Deep Neural Networks โ€” Design by Safa, Review by Yuan-Ting
  • Assignment 7: Structured Prediction โ€” Design by Colin, Review by Yucheng
  • Assignment 8: k-Means โ€” Design by Jyoti, Review by Youjie
  • Assignment 9: Gaussian Mixture Models โ€” Design by Ishan, Review by Colin
  • Assignment 10: Variational Autoencoder โ€” Design by Yuan-Ting, Review by Raymond
  • Assignment 11: Generative Adverserial Network โ€” Design by Ishan, Review by Yuan-Ting
  • Assignment 12: Q-learning โ€” Design by Safa, Review by Youjie

Announcement:

All copyrights reserved ยฉ CS446 Instructors & TAs

  • Raymond Yeh, Website [Link]
  • Colin Graber
  • Safa Messaoud
  • Yuan Ting Hu
  • Ishan Deshpande
  • Jyoti Aneja
  • Youjie Li
  • Yucheng Chen

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machine-learning-uiuc's Issues

Performance issue in the definition of fit, assignments/assignment3/mp3/codefromtf/logistic_model.py

Hello, I found that in the definition of fit, assignments/assignment3/mp3/codefromtf/logistic_model.py, tf.matmul will be repeatedly added to the graph, resulting in a decrease in the running rate of the program. Therefore, I think node A should be created before the loop to reduce unnecessary overhead.

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

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