Teaching

Statistical and Machine Learning (01.113)

WeekTopicLecture slides and HW
1
Regression
Introduction

Regression
2Classification

Classification
3Neural networks and deep learningHW 1

Deep learning

CNNs, Kernel methods

Convolution, pooling animation
4Support vector machinesSupport vector machines

HW 2
5Gaussian processes for regressionGaussian process regression
6Graphical modelsGraphical models

Bayesian networks

Midterm review

HW 3
7Recess (11 - 15 Mar)
8Midterm: 20 Mar, 5-7 pm, MPH, closed book, no cheat-sheets

Clustering
Clustering

Information theory
9EM algorithmEM algorithm
10Variational autoencoders

Principal component analysis (PCA)
VAEs

PCA
11Hidden Markov models

Recurrent neural networks
HW 4

HMMs

RNNs
12Multi-armed bandit problemMulti-armed bandit problem
13Markov decision processesHW 5

Markov decision processes

Final review
14Office hours:
29 Apr, 9:45 am - 11:15 am

Final: 3 May, 3 - 5:30 pm, Sports Hall 1, closed book, no cheat-sheets

TA: Francisco Benita

Email: francisco_benita@sutd.edu.sg

Office hours: Tuesday 12 – 1

Past Courses

(Fall 2018) 10.004: Advanced Mathematics II

(May 23, 2018) NVIDIA Deep Learning Institute (DLI) Workshop:
Fundamentals of Deep Learning for Computer Vision

(Spring 2018) 10-007: Modelling the Systems World

(Fall 2017) Real Analysis