MGT-529 / 3 credits
Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.
Remark: Only given in 2022-23
This class discusses advanced data science and machine learning (ML) topics: Recommender Systems, Graph Analytics, and Deep Learning, Big Data, Data Clouds, APIs, Clustering. The course uses the Wolfram Language. Outcome: coding exercises in ML using real-data and a big ML project deliverable.
- Big Data/Cloud, API
- Association rules
- Recommender system
- Graph Analytics
- Deep Learning
- Applications in Wolfram Language on Text, Audio, Images Analysis
Data science, Machine learning, Algorithms, Big Data, Clustering, Recommender Systems, Deep Learning, Wolfram Language
Data Science and Machine Learning I (MGT-492)
Important concepts to start the course
- Fundamental Probability and Statistics concepts
- Fundamental ML topics: cost function and optimization, gradient descent, K-fold cross-validation, overfitting, model calibration, confusion matrix, curse of dimensionality
- Basic ML methods: Regression (linear regression, ridge regression), Classification (logistic regression, k-NN classification, decision trees, random forests), Dimensionality reduction (PCA, ISOMAP, t-SNE)
- Basic understanding of Neural Networks and Text Analytics (text representation, sentiment classification, similarity search)
- Basic programming skills in Wolfram Language
By the end of the course, the student must be able to:
- Choose an appropriate Machine Learning method for a given task
- Design and Conduct a data science project
- Investigate data, data types, and problems with the data
- Implement ML algorithms in Wolfram Language
- Plan and carry out activities in a way which makes optimal use of available time and other resources.
- Demonstrate the capacity for critical thinking
- Access and evaluate appropriate sources of information.
- Collect data.
- Use a work methodology appropriate to the task.
- Lab sessions: coding exercices
- Data Science projects
Expected student activities
The students are expected to:
- attend lectures and lab sessions;
- work on the weekly theory and coding exercises;
- complete assignments (graded);
- conduct data science projects making use of the theory learned during lectures and code developed during lab sessions (graded)
- Coding assignments: 50%
- Project: 50%
Virtual desktop infrastructure (VDI)
Slides will be made available on the course Moodle page. Notebooks will be made available in a GitHub repository.
Book: Introduction to Machine Learning, Etienne Bernard (2022)
In the programs
- Semester: Fall
- Number of places: 40
- Exam form: During the semester (winter session)
- Subject examined: Data science and machine learning II
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks