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NUS SoC, 2020/2021, Semester I, Time and Venue: fully online via Zoom and YouTube. Officially from LumiNUS: Mondays, 16:00-18:00 and Thursdays, 11:00-12:00.

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This module introduces basic concepts and algorithms in machine learning and neural networks. The main reason for studying computational learning is to make better use of powerful computers to learn knowledge (or regularities) from the raw data. The ultimate objective is to build self-learning systems to relieve human from some of already-too-many programming tasks. At the end of the course, students are expected to be familiar with the theories and paradigms of computational learning, and capable of implementing basic learning systems.

We will be using the Coursemology Learning Management System for the administration of this course (the Coursemology course will be published in due time).

N.B. We will be teaching and using the Python programming language throughout this class and Jupyter Notebook via Google Colab. We will using Python 3.x, and largely the SciKitLearn and PyTorch libraries.

Class Structure

This class is a flipped class, a variant of a blended class. You’ll watch the first part of the video lecture before coming to tutorial, and then watch a subsequent video recorded lecture post-tutorial, to further reinforce the tutorial.

Important: Lecture slots are for e-learning, and for occasional assessments (in Weeks 07 and 13).
Do ensure that you have the lecture times free in your schedule to be available for scheduled exams. There will no be alternate exam arrangements.

For most of the Thursdays sessions, we will have a synchrnous help session where Min will field questions from students on questions, concerns and doubts you have on the lecture materials or other components of the course.

Tutorial Sessions

There will be tutorials for this class starting in Week 03. As the class is flipped, these sessions will be the primary means by which we touch base with you and get to know you personally. Please do attend these sessions via Zoom, as they will not be webcasted (although tutorial solutions will be distributed, you should come to the sessions to get the complete picture, and to be a part of the class).

These tutorial session timings still subject to change. Please see NUSMods for the most up-to-date details. As an enrolled student, you are entitled to one tutorial placement, and need to attend that slot even if not optimal for you. Nicely, all of the tutorials slots come before (Monday-Wednesday), the second class lecture slot on Thursdays.

Tutorial Sessions (tentative; as reported by LumiNUS ca. 17 Jun 2020):

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1. T01. Mondays, 13:00-14:00
2. T02. Wednesdays, 17:00-18:00
3. T03. Mondays, 15:00-16:00
4. T04. Mondays, 14:00-15:00
5. T05. Wednesdays, 11:00-12:00
6. T06. Mondays, 12:00-13:00
7. T07. Wednesdays, 16:00-17:00
8. T08. Wednesdays, 10:00-11:00
9. T09. Tuesdays, 16:00-17:00
10. T10. Tuesdays, 17:00-18:00
11. T11. Tuesdays, 12:00-13:00 (new as of 11 Aug 2020)
12. T12. Tuesdays, 13:00-14:00 (new as of 11 Aug 2020)

Course Characteristics

Modular Credits: 4.

Prerequisites: (CS2010 or its equivalent) and (ST1232 or ST2131 or ST2132 or ST2334) and (MA1101R or MA1311 or MA1506) and (MA1102R or MA1505 or MA1521)

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Translation: Linear algebra, calculus, probability and statistics and introductory computer programming.

Questions about prerequisities and waivers are handled centrally by the department. Please contact OOI Wei Tsang) to describe your case and seek any waivers regarding prerequisities.

Instructors

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Teaching Assistants

Graduate Teaching Assistants

Undergraduate Teaching Assistants

Workload

(3-1-0-3-3)

Translation:

3 lecture hours per week (flipped) 1 hour of tutorials 3 hours for projects, assignments, fieldwork, etc. per week 3 hours for preparatory work by a student per week

Slot machines are the most popular game in modern casinos. If you’ve never seen one, a slot machine resembles an arcade game that has a lever on its side. For a small fee you can pull the lever, and the machine will generate a random combination of three symbols. If the correct combination appears, you can win a prize, maybe even the jackpot.

Slot machines make fantastic profits for casinos because they offer a very low payout rate. In many games, such as Blackjack and Roulette, the odds are only slightly stacked in the casino’s favor. In the long run, the casino pays back 97 to 98 cents in prizes of every dollar that a gambler spends on these games. With slot machines, it is typical for a casino to only pay back 90 to 95 cents—and the casino keeps the rest. If this seems underhanded, keep in mind that slot machines are one of the most popular games at a casino; few people seem to mind. And if you consider that state lotteries have payout rates that are much closer to 50 cents on the dollar, slot machines don’t look that bad.

In this project, you will build a real, working slot machine modeled after some real life Video Lottery Terminals from Manitoba, Canada. The terminals were a source of scandal in the 1990s. You’ll get to the bottom of this scandal by writing a program that recreates the slot machines. You’ll then do some calculations and run some simulations that reveal the true payout rate of the machines.

This project will teach you how to write programs and run simulations in R. You will also learn how to:

• Use a practical strategy to design programs
• Use if and else statements to tell R what to do when
• Create lookup tables to find values
• Use for, while, and repeat loops to automate repetitive operations
• Use S3 methods, R’s version of Object-Oriented Programming
• Measure the speed of R code
• Write fast, vectorized R code