None.  Nodding acquaintance with programming would possibly help.  Need more than a faint heart.

Syllabus 

Listed topics may not be taught in a strict order, and are only illustrated with typical currently popular machine-learning methods as placeholders.

1.      Artificial intelligence and machine learning framework:
         A survey of current development and future directions.

2.      Python programming basics:
         The Python programming environment for software development; performing hands-on coding for a simplified routine recursive task.

3.      Statistics for machine learning:
         Central tendency, dispersion measures, probability distributions, sampling schemes, and correlations.

4.      Supervised and unsupervised learning:
         Survey operations carried out on columns or rows to learn structures and build models.

Supervised Learning:

5.      Stochastic Gradient Descent, Elastic Net and Support Vector Regressors:
         Introduction to pathfinding in optimization; extensions to ordinary regression; and searching for delineating gaps between groups of data rows.

6.      Time Series Forecast models:
         Methods for sequentially collected data.

7.      Random Forest Regressor and Gradient Boosting Machine:
         Ensemble of simultaneous non-linear predictive models using bifurcating branches; sequential strengthening of weak predictors via a differentiable loss
         function. 

8.      Naïve Bayes Classifier and Decision Tree Classifier:
         Categorizing classes using conditional probabilities; constructing triage rules via measures of diversity.

9.      K-Nearest Neighbors Classifier and Support Vector Classifier:
         Classifying objects by majority votes of neighbors; Classifier counterpart to Support Vector Regressors.

Unsupervised Learning:

10.   K-Means Clustering and Hierarchical Clustering:
        Unsupervised grouping of rows by distances from center points; grouping of rows using hierarchy of clusters.

11.   Principal Component Analysis:
       Grouping of weighted columns along perpendicular directions.

Reinforced Learning:

12.   Dynamic Optimization:
        Finding the sequential maximum or minimum of a combination of transformations of columns.

Assessment

 Three individual modelling assignments (of 15% each) will require students to use specific models (just covered in class at that point) to extract information for decision making, via supplied python templates by using them on non-trivial datasets.  Write-ups for individual assignments should not exceed 3 concise pages in A4 format.

The Analytics Report Writing Exercise will require students to artfully summarize a simple technical article for the typical literate but non-numerate magazine reader.

The Group Case Study involves the handling of a smaller dataset for a more elementary task, just to get the group members to work together and learn from each other early in the semester.

For the Project, each group will need to choose a few methods covered in the module to process a large, assigned dataset or one culled by the students from other disciplines, e.g., behavioral & consumer science, biomedicine, environmental studies, etc.  Students will be given few explicit instructions for the completion of the Group Project.  This will be meant as a simulation of an encounter with real, messy data while having no guidelines, as might happen in a future workplace.

Each student will need to submit a 300-word article—written for the technically-savvy layman—on their personal encounter with the analytics methods used by the Group for the Project, which will account for 5% out of the 30% for the Project grade.  Students will still share the same marks for the remaining 25% of the module grade for the Project.  Every student will articulate in their respective Group Project presentation for a finale.

The Project, also providing peer-teaching opportunities, is carried out in groups of not more than 5 students. The write-up should not exceed 5 concise pages in A4 format.

Assessment for Individual Participation will include a student’s attitude, behaviour & etiquette, attendance record, punctuality, participation in online discussion forum, etc.

Suggested Preliminary Reading

https://docs.microsoft.com/en-us/learn/modules/secret-message/0-introduction  Learn Python basics with Wonder Woman.