Semester 2, 2017/2018
YaleNUS College
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.
The following schedule will not be strictly followed.
Date   

August 15 
1. LEAST SQUARES
Reading:
Additional resources (optional): 
Lecture note 1
Assignment 1 
August 8 
2. INTRO TO BAYESIAN INFERENCE
Reading:
 Lecture note 2 
August 22 
3. MLE FOR REGRESSION
Reading:
Additional resources:
 Lecture note 3 
August 25 
4. BASIS FUNCTIONS
Reading:
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)
