Learning Outcomes
Proficiency in Data Mining Techniques:
- Understanding Algorithms: Mastery of various data mining algorithms for clustering,
association rule mining, anomaly detection, etc.
- Application Skills: Ability to apply these algorithms to different datasets and scenarios
effectively.
Expertise in Recommender Systems:
- Comprehensive Knowledge: Understanding different types of recommender systems and their
implementation.
- Hands-on Experience: Practical experience in building and evaluating recommendation
models.
Data Handling and Analysis Skills:
- Data Preprocessing Mastery: Proficiency in data cleaning, transformation, and preparation for
analysis.
- Dimensionality Reduction Techniques: Understanding and applying methods like PCA/SVD for efficient data representation.
- Feature Selection: Ability to select relevant features for analysis and model building.
- Anomaly Detection: Capability to identify outliers and anomalies within datasets.
Time Series Analysis Competency:
- Understanding Time Series Data: Proficiency in analyzing time-dependent data using
techniques like ARMA/ARIMA.
- Forecasting Skills: Ability to make predictions and forecasts based on time series analysis
User Profiling and Content Filtering Skills:
- User Profiling: Understanding user behavior and creating profiles for personalized services or
recommendations.
- Collaborative Filtering: Ability to utilize collaborative filtering techniques, including matrix/tensor
factorization.
- Content Filtering: Understanding and implementing content-based recommendation methods.
Practical Application and Research:
- Hands-on Project Work: Experience in implementing learned concepts in real-world datasets.
- Research Skills: Ability to explore advanced topics and potentially contribute to research in the
field.
Critical Thinking and Problem-Solving:
- Analytical Skills: Enhanced ability to analyze complex datasets and derive meaningful insights.
- Problem-Solving Approach: Proficiency in addressing data-related challenges using
appropriate methods and tools.
Course Content
Classification
Clustering
Data preprocessing
Dimensionality reduction
PCA / SVD
Feature selection
Anomaly detection
Timeseries analysis (ARMA / ARIMA)
Association rule mining
User profiling
Content filtering
Collaborative filtering, incl. Matrix / tensor factorization
