| Category | Difficulty |
|---|---|
| Assignments | 4 |
| Quiz | 2 |
| Exams | 4 |
| Kaggle challenge | 3 |
Data Inference and Applied Machine Learning is an introductory course on the concepts of data science. It is offered by Prof. McSharry and is usually streamed from the CMU campus in Rwanda. The course is available to students in Pittsburgh and Silicon Valley and is good for beginners in the field of data science and machine learning. However, if you already have knowledge and experience in the field, you may find this a bit boring.
The course is structured into two parts. The first part is Data Inference which focuses on learning how to utilize data using descriptive statistics and data analysis. Some of the topics covered in this part include:
- Data Collection
- Data Manipulation and exploration
- Descriptive statistics
- Distributions
- Statistical hypotheses
The second part is applied machine learning which focuses on using machine learning models in modelling data to extract information, insights and support decision making. Some of the topics covered in this part include:
- Trends and Desicion Making
- Forecasting
- Model evaluation
- Statistical learning
- Linear models
- Non-linear models
- Supervised and unsupervised learning
- Ensemble techniques
There are 7 assignments in the course. The first assignment is weighted 5%, while the last two assignments are each weighted 12.5%. The rest of the assignments are each weighted 10%. The assignments are based on practical applications on data analysis and machine learning and they can be done in MATLAB or Python. Starter code is provided for some of the more challenging assignments during recitation. There's also a Kaggle project at the end where you apply the ML techniques in a machine learning challenge.This is given as a bonus score at the end.
There are mid-term and Final exams each weighted at 10%. In each class, there will be a kahoot quiz competition to test your understanding of the topics covered in the previous class. These quizzes are weighted as 2.5% of the grade. They are usually fun since the winners are displayed on the leader board before the class starts.
Participation in class is also weighted at 5%. This is usually detected in the form of contributions on the class Piazza.
Attend all the classes and do the quizzes in each class. Make sure to perform well in most if not all the quizzes. The trick to this is to go through the lecture notes of the previous class before the quiz. If you do this you are guaranteed to get a high score in all the kahoot quiz competitions.
Also ensure that you start the assignments early and attend office hours to seek help whenever you are stuck. Use piazza to ask questions since this will also count as your participation score.
The latter part of the course gets a bit complex especially when doing the assignments on machine learning applications. Start those assignments early and make use of the materials given during recitation and TA office hours.