Machine Learning - A non-technical introduction with applications to Marketing

Objective:

Machine Learning has become one of the core pillars of information technology. Since the amount of available data is steadily increasing, smart data analysis will become more and more important in the future. This course introduces Machine Learning in a non-technical, hands-on way with integrated exercises and group works.

A definition of Machine Learning, sampling and cross-validation, performance evaluation, logistic regression, decision trees, random forest, deep learning, and ensemble methods are among the topics to be discussed in this course.

The learning objectives of this course are as follows:

• Get familiar with the concept of machine learning.
• Understand the basic theory behind various machine learning techniques.
• Apply different machine learning techniques and interpret the results.

Contact:
market-research@business.uzh.ch

Type:
Lecture

Target audience:
MA students, assigned to “Wahlpflichtbereich BWL 4”

Frequency:
Each Fall Semester

AP(ECTS)-points:
3

Language:
English

Required reading:
Hastie, T., Tibshirani R., Friedman, J. (2013): The Elements of Statistical Learning – Data Mining, Inference, and Prediction, 2^nd edition, Springer.

(available here)

Previous knowledge:
Recommended: Marketing Analytics, A non-technical introduction to R

Grading:
The following components comprise the final grade:

1. 1. Daily coding exercises: every day of the class (20%): on-site assessments (computer-based, BYOD format; bring a charger)
2. 2. Kaggle competition (10%): on-site assessment (computer-based, BYOD format; bring a charger)
3. 3. Final exam on 06.09.2024 (50%): on-site, written examination, multiple choice format
4. 4. Online exercises on DataCamp (to be completed after the course) (20%)

Dates and Location:
Block Course: 02.09.2024 to 06.09.2024, daily from 9:00 to 17:00
Location: AND-4-06 (Mon & Fri) and BIN-0-K.11 (Tue-Thu)

Registration:
Don’t forget to officially register yourself using the registration tools at the University of Zurich.

Note:
The information in the website or syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In case of doubt, the official information at the VVZ is valid.