Course Instructor:
Luca Arrotta is Ph.D. student at the Department of Computer Science of the University of Milan. His interests are Machine Learning, Data Analysis, IoT, Mobile Programming, and Indoor Positioning. His research currently focuses on Pervasive Computing, Context-awareness, Explainable AI, and Human Activity Recognition in smart environments.
Course Overview & Lectures
Duration: 1.5 hours
Intro
Lecture 1: Course Introduction
Python Libraries
Lecture 2: Machine Learning Introduction
(Machine Learning (ML) Introduction: -AI, Machine Learning, Deep Learning -Supervised/unsupervised with subcategories -Reinforcement Learning and Semi-supervised (brief introduction) -ML pipeline -Overfitting/Underfitting)
Lecture 3: Machine Learning Libraries
(Numpy: array operations, matrix operations Pandas: csv and dataframes management and analysis Visualization Tools: matplotlib and seaborn Other libraries: SciPy, Scikit-Learn, Keras)
Lecture 4: Github Notebook-1
Machine Learning Pipeline
Lecture 5: Machine Learning Pipeline
(Get or collect data, Data pre-processing (cleaning, normalization, standardization, dimensionality reduction), Feature extraction and selection (with Deep Learning?), Model selection (development, grid search tuning), Model evaluation (K-fold, confusion matrix etc.)
Lecture 6: Lab ML Pipeline
Lecture 7: Github Notebook-2