aoml/course_syllabus.md

# Applied Optimization Techniques

## Course goals

The goal of this course is to provide an introduction to simulation,
optimization and machine learning techniques to students with a background in
social sciences, with an approach biased towards practical work. The expected
outcome is that students that have passed this course know a variety of modern
and useful techniques that can be applied in real-life business contexts. With
this knowledge and experience, the students understand what are the right
techniques for different problems, which are the main steps and requirements to
apply each of these techniques and how to judge the successful application of
them.

Many of the techniques taught in this course are usually taught to engineering
and technical profiles. This course does not aim to bring students to the same
level of technical expertise as their engineering counterparts, but rather to
provide enough background so that the students can successfully interact with
such profiles. Having said that, this course can also be a first introduction
for students that are willing to pursue a more thorough learning of the
techniques discussed in the course, after or during itself.

With the knowledge and skills obtained in this course, students become fit for
tasks such as:

- Applying simulation, optimization and machine learning techniques to simple
  cases.
- Planning and designing simulation, optimization and machine learning
  initiatives.
- Leading simulation, optimization and machine learning projects from a
  managerial point of view.
- Acting as a liaison between management and technical profiles in business
  contexts.

## Pre-requisities

The course assumes the student has covered Mathematics I, II, III courses and
the Probability & Statistics course. Passing this course is not impossible if
that is not the case, but the student should expect a non-trivial challenge
ahead.

Knowledge of the following topics will help students better leverage this
course, but is not strictly required:

- Basic programming, specially in data oriented languages such as Python or R.
- Operations research

## Teaching method and contents

The course will have lecture classes and practical seminars. Classes start on
April 7th.

There will be 20 lecture classes and 6 practical seminars. Lecture classes will
be used to present material to students as well as having discussions on the
course contents. For the practical seminars, students will be divided into two
groups with independent sessions to reduce the class size. The practical
seminars will be used to deep-dive in the three mandatory case assignments that
students will do throughout the course. The sesions will also be hands-on and
students will work in the case together with the professor.

Students are expected to attend all the activities in the course. Beyond
lectures and practical seminars, additional reading resources will be provided
to students. For students that need to level up their Python skills, self-paced
materials will be suggested.

Lectures will have the following contents:

| Week | Classes                                                                                                                                                                | Student work                                                                                                                                                                                                                                                                                                |
|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1    | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies                                                  | - Python prep                                                                                                                                                                                                                                                                                               |
| 2    | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1.                 | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1.                                           |
| 3    | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics)                                                                                                              |
| 4    | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1                                                            | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) |
| 5    | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2                                                        | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html)  |
| 6    | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2                        | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf)                                                                                                                                                                       |
| 7    | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2                                 | - Work on case 2 <br/> - Read case 3                                                                                                                                                                                                                                                                        |
| 8    | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3                                              | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ)                                                                                                            |
| 9    | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3                                   | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0)                                                                                                                                                                                        |
| 10   | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation                                                              | - Work on case 3                                                                                                                                                                                                                                                                                            |
| 11   | - Exam                                                                                                                                                                 | - Deliver case 3                                                                                                                                                                                                                                                                                            |                                                                                                                                                         |                                                                                                                                                                                                                                                                                                         |

- Lecture 1 INTRO
    - Introduction to the course
        - Citizenship rules
            - Won't force you to come, but I advice you to.
            - I'll always try to start 5min late, finish 5min late, and stop
              for 5min.
            - You can come and go, just please be respectful.

        - Calendar
        - Contents
        - Expectations
        - The teacher
        - Evaluation
        - Contact
        - Questions?
        - The relevance of math and computers in management
            - Examples: pricing, logistics, staffing.
            - The skills and profiles required
            - The tools used
- Lecture 2 INTRO
    - The techniques we will see in the course
        - Simulation
        - Optimization
        - Supervised machine learning (aka "prediction")
    - Why this stuff is important
- Lecture 3 SIM
    - A humbling example
    - What is simulation and when do we use it
    - Different types of simulations
- Lecture 4 SIM
    - Toy simulations in Python
    - How to approach simulation in practical terms
    - Tools in industry
- Lecture 5 SIM
    - Theoretical background on simulation
    - Present case 1
- Lecture 6 SIM
    - Simulation-based optimization
    - Where to go from here
- Lecture 7 OPT
    - What is optimization
    - A trivial example
- Lecture 8 OPT
    - Different optimization techniques
    - Present case 3
- Lecture 9 OPT
    - How to model optimization problems (target functions, decision variables
      and constraints)
- Lecture 10 OPT
    - Simulation-based optimization: Genetic algorithms
- Lecture 11 OPT
    - Real world challenges and optimization deployment
- Lecture 12 ML
    - Good news, you already know Machine Learning
    - Different branches of Machine Learning
    - Real world examples of applications
- Lecture 13 ML
    - How does Supervised Machine Learning work?
    - Present case 2
- Lecture 14 ML
    - The Machine Learning workflow (EDA, Feature Engineering, Model
      Evaluation, Deployment)
- Lecture 15 ML
    - Feature Engineering
- Lecture 16 ML
    - Model evaluation
- Lecture 17 ML
    - Deployment and real world challenges
- Lecture 18 Real life stories from the trenches
- Lecture 19 Real life stories from the trenches
- Lecture 20
    - Q&A pre-exam
    - Feedback on the course

## Case details

Case 1

- Title: Choosing stock policies under uncertainty
- Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Elisa, the
  regional manager for the italian region, is not happy with their inventory
  policies for raw beans. The students are asked to analyse the problems posed
  by Elisa and apply simulation techniques, together with real data, to
  recommend a stock policy for the company's warehouse in the italian region.
  Python notebooks with some helpful prepared functions are provided to the
  students. The final delivery is a report with their recommendation to the
  client company, along with the used code.

Case 2 candidate

- Title: ?
- Description: ?
- Sample idea: https://www.gurobi.com/resource/facility-location-problem/

Case 3

- Title: Improving last-mile scheduling with Machine Learning
- Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Pieter, the
  director of secondary transportation, has requested help from the student
  consultants. One of the key activities in Pieter's team is the daily
  scheduling, where the different trucks get assigned which deliveries and
  routes will perform. The students are asked to develop a machine-learning
  algorithm to predict the drop-time for each delivery (the drop-time is the
  time a driver takes in unloading the goods in a a client location. More
  informally, the time that passes since he removes the key from the truck
  until he starts the engine again). The goal is to provide more advanced
  information for Pieter's schedulers so they can better plan the routes of
  their drivers. The students are asked to build and deliver a Machine Learning
  algorithm that predicts this time. The students will be provided a labelled
  dataset. The final delivery is the working prediction model, along with a
  report explaining their methodology in building it, and answering some
  business questions to the client company.

## Grading

The following items compose the final grade:

- Case assignments: 50% of the grade. There will be three assignments, each
  with the same weight. The average grade of the assignments must be of 5 or
  more to pass the course.
- Final exam: 50% of the grade. There will be a final exam at the end of the
  course. The grade must be of 5 or more to pass the course.


A final grade is calculated as:

<!-- @formatter:off -->

```python
if avg(case1_grade, case2_grade, case3_grade) < 5:
    passed_course = False
if final_exam_grade < 5: 
    passed course = False

passed_course = True
final_grade = (avg(case1_grade, case2_grade, case3_grade) + final_exam_grade) / 2
```

<!-- @formatter:on -->

## Bibliography

All compulsory and required materials will be provided during the course. These
include lecture notes, required readings and description readings.

A good book that follows the approach of this course is "Guttag, John.
Introduction to Computation and Programming Using Python: With Application to
Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624", used in the
homonymous course at MIT. It is not compulsory to use this book, but some
students might find it helpful.

Additional specific readings will be provided throughout the course. Students
will be requested to read some of these materials in advance of some sessions.

For students that want to dive deeper in the topics covered in the course, the
following books are recommended:

- On simulation: Louis G. Birta Gilbert Arbez, Modelling and Simulation.
  Springer 2019 ISBN: 978-3-030-18869-6 or Law A., Kelton D., Simulation and
  Modelling Analysis, Second Edition, McGraw-Hill, ISBN: 978-0071165372
- On machine learning: Hastie T., Tibshirani R., Friedman J., The Elements Of
  Statistical Learning: Data Mining, Inference, And Prediction, Second Edition
  ISBN: 978-0387848570

## Cool ideas & notes

- Hold a Kaggle competition with the students. Winners come to spend a morning
  in Accenture.
- Start every lecture with a fun fact.
- Let them choose the challenge for one of the practical labs.
- Should I have office hours?
- Will the classes be recorded?
- What's are the policies on:
    - Late deliveries
    - Not attending exam
    - Re-takes
- https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016