YSC3227 Machine Learning

Semester 2, 2017/2018
Yale-NUS College



Description:

The goal of machine learning is to enable machines/computers to identify patterns from data, extract the patterns, and based on them, make an inference or prediction automatically. These capabilities are the core of artificial intelligence (namely, to make machines learn without being explicitly programmed using fixed predetermined rules). The applications of machine learning are immense, since nowadays we are bombarded with a huge number of various data from various sources. We hope machine learning can make sense of this huge seemingly random data.

This course focuses on the fundamentals of machine learning, including and deep learning. It should interest students who want to study/work in big data, AI (artificial intelligence), and data science.


Prerequisite: Programming skill in Python.
Textbook: "Pattern Recognition and Machine Learning", by Christopher Bishop.
Instructor: Robby T. Tan (robby.tan [att] yale-nus.edu.sg)


Schedule:

The following schedule will not be strictly followed.


Date
Topic
Lecture Note
August 15 1. LEAST SQUARES

Reading:
  • Chapter 1 (Introduction): Sect. 1.1 (Polynomial Curve Fitting)

Additional resources (optional):
Lecture note 1
Assignment 1
August 8 2. INTRO TO BAYESIAN INFERENCE

Reading:
  • Chapter 1 (Introduction): Sect. 1.2 (Probability Theory), Sect. 1.3 (Model Selection)
  • Introduction to Bayesian inference: video

Lecture note 2
August 22 3. MLE FOR REGRESSION

Reading:
  • Bayesian Inference: An Introduction to Principles and Practice in Machine Learning (Sect. 2.1, 2.1.1, and 2.1.2 only): pdf

Additional resources:
Lecture note 3
August 25 4. BASIS FUNCTIONS

Reading:
  • Chapter 3: Linear Models for Regression (Sect. 3.1 only)

Additional resources:
August 29 5. GAUSSIAN DISTRIBUTIONS: COVARIANCE MATRIX

September 5 6. CONDITIONAL AND MARGINAL GAUSSIAN DISTRIBUTIONS

September 8 7. BAYES' THEOREM FOR GAUSSIAN VARIABLES

September 12 8. SEQUENTIAL BAYESIAN LEARNING

September 15 9. REVIEW: METHODS FOR ESTIMATING W

September 19 10. PREDICTIVE DISTRIBUTION

September 22 11. GAUSSIAN PROCESSES 1

RECESS WEEK

October 3 12. GAUSSIAN PROCESSES 2

October 6 13. CLASSIFICATION: LEAST SQUARES

October 10 14. FISHER'S LINEAR DISCRIMINANT ANALYSIS

October 13 15. DETAILS OF FISHER'S LDA

October 17 16. PROBABILISTIC GENERATIVE + DISCRIMINATIVE MODELS

October 20 17. BAYESIAN LOGISTIC REGRESSION

October 24 18. GAUSSIAN PROCESSES FOR CLASSIFICATION

October 27 19. GAUSSIAN PROCESSES FOR CLASSIFICATION: 2

October 31 20. NEURAL NETWORKS

November 3 21. NEURAL NETWORKS: BACKPROPAGATION

November 7 22. DEEP LEARNING: FURTHER ISSUES

November 10 23. DEEP LEARNING: ARCHITECTURES

November 14 24. MIXTURE MODELS + EM ALGORITHM

November 17 25. DISCUSSION

December 1 FINAL EXAM: 1pm to 4pm (Classroom 20)





Syllabus: