Machine Learning Basics (MSc)

 

Lecture Slides
Lecture Notes
AI-900 materials (for AI-900 Microsoft Azure AI Fundamentals Certification)
Exercises


Topics:

 

1.     Basic concepts

2.     Linear Algebra

3.     Probability and Information Theory

4.     Numeric Computations

5.     Data Preprocessing

6.     Dimensionality Reduction

7.     Regression Models 1

8.     Regression Models 2

9.     Classification 1

10.  Classification 2

11.  Clustering 1

12.  Clustering 2

13.  Association Rule Learning

14.  Reinforcement Learning

 

Literature:

ˇ       W. McKinney: Python for Data Analysis (1 ed.). O'Reilly Media, Inc. 2012.

ˇ       Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006.

ˇ       D. Conway, J.M. White: Machine Learning for Hackers, O'Reilly Media, Inc., 2012.

ˇ       I. Goodfellow, Y. Bengio, A. Courville: Deep Learning, MIT Press, 2016.

 

Main concepts:

 

Supervised Learning, Unsupervised Learning, Linear Regression (one/multiple variables), Gradient Descent, Feature Normalization, Polynomial Regression, Normal Equation, Logistic Regression, Binary Classification, Multiclass Classification (One-vs-all), Regularization (overfitting, underfitting), Regularized Linear Regression, Regularized Logistic Regression, Neural Networks, Backpropagation Algorithm, Gradient Checking (numerical), Train/Validation/Test Sets, Diagnosing Bias vs. Variance, Regularization and Bias/Variance, Learning Curves (training set size), Error Metrics for Skewed Classes, Support Vector Machine, Kernels in SVM, Clustering, K-Means Algorithm, Choosing the Number of Clusters, Dimensionality Reduction (PCA), Anomaly Detection, Gaussian Distribution, Multivariate Gaussian Distribution, Recommender Systems, Content Based Recommendations, Collaborative Filtering, Stochastic Gradient Descent, Mini-Batch Gradient Descent, Map Reduce and Data Parallelism

 

Programming tasks:

 

- SPAM filtering (larger database)

- Anomaly detection vs supervised learning

- Transforming data to Gaussian with random sample

- K-means clustering with adding new clusters

- Neural nets for logical operators

- Handwritten characters classification

- Recommendation system for own dataset