ECE 2195 Machine Learning
Fall 2020

Heng Huang
[ Administrative Basics | Course Description | Assignments | Syllabus ]

Administrative Basics

Lecture

Carnegie Museum of Art Lecture Hall | Thursday 2:50PM - 5:15PM | The lectures will be taught via Zoom and the students can choose to use Zoom or classroom
Instructor

Heng Huang | Zoom Room | Office hours: Thursday 11:00am-1pm
Textbook

Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
Other readings

Pattern Classification, 2nd edition, Richard Duda, Peter Hart, David Stork.
Deep Learning, Aaron Courville, Ian Goodfellow, Yoshua Bengio.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, T. Hastie, R. Tibshirani, J. H. Friedman.
Work

Homework sets. (75%)
Final project. (25%)
Request

Good math, statistics, and programming background.

Course Description

This course is for the entry graduate level students to study the background in the methodologies, mathematics and algorithms in machine learning or who may need to apply machine learning techniques to scientific applications (e.g. computer vision, bioinformatics, data mining, information retrieval, natural language processing, etc). The following broad categories will be covered:
1) Introduction to Pattern Recognition and Machine Learning
2) Regression
2) Bayesian Learning
3) Linear Discriminants
4) Neural Networks and Deep Learning
5) Support Vector Machines and Kernel Method
6) Decision Trees
7) Feature Selection
8) Model Selection
9) Unsupervised Learning
10) Graphical Model
11) Semi-Supervised Learning Methods

Assignments

Homework Projects

Homework project sets will be assigned in canvas.
Final Project

Final project will be assigned in canvas.

Syllabus

  • Week 1 (Aug 20):
    • Introduction to Machine Learning
    • Basic Machine Learning Knowledge
  • Week 2 (Aug 27):
    • No class (the instructor is organizing the ACM KDD conference at this week).
  • Week 3 (Sep 3):
    • Naive Bayes Classifier
    • Logistic Regressions, Linear and Quadratic Discriminant Analysis Classifiers
  • Week 4 (Sep 10):
    • Linear Algebra Review
    • Linear Support Vector Machine
  • Week 5 (Sep 17):
    • Kernel Support Vector Machine
    • Kernel Methods, and Support Vector Regression
  • Week 6 (Sep 24):
    • Dimensionality Reduction: Fisher Linear Discriminant Analysis
    • Dimensionality Reduction: PCA and SVD
  • Week 7 (Oct 1):
    • Validations, KNN, and Clustering
    • K-means and EM Algorithm
  • Week 8 (Oct 8):
    • Spectral Clustering and Graph Cut
  • Week 9 (Oct 15):
    • Nonegative Matrix Factorization
    • Kernel PCA, Tensor Factorization
  • Week 10 (Oct 22):
    • Neural Network and Deep Learning I
    • Neural Network and Deep Learning II
  • Week 11 (Oct 29):
    • Neural Network and Deep Learning III
    • Neural Network and Deep Learning IV
  • Week 12 (Nov 5):
      Sparse Learning and Dictionary Learning
    • Feature Selection
  • Week 13 (Nov 12):
    • Semi-Supervised Learning
    • Boosting and Decision Tree
  • Week 14 (Nov 19):
    • Hidden Markov Models
    • Conditional Random Field
    • Markov Random Field
  • Week 15 (Nov 24):
    • Thanksgiving Day, No Class