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fi3271-01 2024-2

· 3 mins read

FI3271 Data Analysis with Machine Learning is a 3-hours course given in about 15-16 weeks. It is a part of 2024 curriculum .

topics

  1. Computational thinking and algorithm design
  2. Concepts in machine learning: supervised and unsupervised learning
  3. Probability distributions and their applications
  4. Linear models for regression and classification
  5. Sampling methods and their role in data analysis
  6. Artificial neural networks: structure and training
  7. Gaussian process regression for atomic force field modeling
  8. Genetic algorithms and evolutionary computation
  9. Bayesian optimization and its applications

learning outcome

  1. Understand and explain the basic concepts of computational thinking
  2. Design algorithms to solve problems involving physical systems and data
  3. Apply machine learning techniques to analyze data from physical systems
  4. Present scientific findings clearly in both written reports and oral presentations
  5. Collaborate effectively in teams and work independently when needed

conducted plan (fm)

WeekTopic ↓ Subtopic
 Computational thinking and algorithm design
01.1Concept of computational thinking
 Abstraction and decomposition
 Pattern recognition
01.2Algorithmic thinking
 Sampling methods and their role in data analysis
02.1Data mining
 Data preprocessing
 Linear models for regression and classification
02.2Linear regression
04.1Classification with Support Vector Machine (SVM)
 Quiz
04.2Support Vector Machine kernels
 Concepts in machine learning: supervised and unsupervised learning
03.1Principal Component Analysis 1
03.2Principal Component Analysis 2
05.1Classification with k-nearest neighbors (k-NN) algorithm 1
05.2Classification with k-nearest neighbors (k-NN) algorithm 2
 Artificial neural networks: structure and training
06.1Concept of Artificial Neural Network (ANN)
06.2Architecture of Multi-Layer Perceptron (MLP) 1
07.1Architecture of Multi-Layer Perceptron (MLP) 2
07.2ANN basic method hands-on
08.1Midterm
08.2ANN hierarchy design
 Independent assignment

tentative plan (sv)

WeekTopic ↓ Subtopic
 Probability distributions and their applications
09.1Introduction to probability distributions
 Common probability distributions in Machine Learning
09.2Applications in Machine Learning
 Gaussian process regression for atomic force field modeling
10.1Background concepts
 Representing atomic environments
 Model training and prediction
 Force field construction
10.2applications
 Software and tools
 Limitations and challenges
 Genetic algorithms and evolutionary computation
11.1Foundations of Evolutionary Computation
 Genetic Algorithms (GAs)
 Evolutionary Strategies (ES)
 Genetic Programming (GP)
 Differential Evolution (DE)
 Other Evolutionary Algorithms
 Hybrid and Memetic Algorithms
11.2Theoretical Aspects
 Applications
 Tools and libraries
 Benchmark problems
 Recent advances and research trends
 Bayesian optimization and its applications
12.1Foundations of Bayesian optimization
 Advanced techniques
 Algorithmic implementations
12.2Applications
 Applications in Machine Learning
 Applications in Science and Engineering
 Applications in Business and Operations
 Software and tools
 Recent research trends
 Research-Based Learning (RBL)
13.1Topics proposing and discussion
 Working group creation
13.2Short presentasion
14.1Progress report presentation 1
14.2Progress report presentation 2
15.1Progress report presentation 3
15.2Progress report presentation 4
16.1Final report presentation
16.2Publishing final report on Medium, OSF, YouTube

notes

  • Information from SIX are gathered and mixed with lecturers discussion.
  • There four references from course syllabus 1, 2, 3, 4, where the first two are main references, while the others are additional ones.
  • There is an additional reference for information related to probability distributions 5.

refs


  1. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, 1st edition, Springer, 2006, url https://isbnsearch.org/isbn/9780387310732 2dn86 [20250420]. ↩︎

  2. S. Haykin, “Neural Networks and Learning Machines”, 3rd edition, Pearson Education, 2009, url https://isbnsearch.org/isbn/9780131471399 b69e4 [20250420]. ↩︎

  3. Stuart J. Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach”, 3rd edition, Pearson Education, 2016, url https://isbnsearch.org/isbn/9780136042594 s3keu [20250420]. ↩︎

  4. Alberto Artasanchez, Prateek Joshi, “Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x”, 2nd edition, Packt Publishing, 2020, url https://isbnsearch.org/isbn/9781839219535 eb3p2 [20250420]. ↩︎

  5. T. T. Soong, “Fundamentals of Probability and Statistics for Engineers”, Wiley, 1st edition, 2004, url https://isbnsearch.org/isbn/9780470868140 [20250420]. ↩︎