Write me a 13-weeks syllabus for an academic course named "Machine Learning & Data Mining" with the following sources as the bibliography: Pattern classification, Duda, Hart, Stork, 2nd edition, 2001 
Machine Learning, Tom Mitchell, McGraw Hill 1997
user
Write me a 13-weeks syllabus for an academic course named "Machine Learning & Data Mining" with the following sources as the bibliography: Pattern classification, Duda, Hart, Stork, 2nd edition, 2001 
Machine Learning, Tom Mitchell, McGraw Hill 1997
assistant
Write me a 13-weeks syllabus for an academic course named "Machine Learning & Data Mining" with the following sources as the bibliography: Pattern classification, Duda, Hart, Stork, 2nd edition, 2001 
Machine Learning, Tom Mitchell, McGraw Hill 1997
user
Course Title: Machine Learning & Data Mining
Duration: 13 weeks
Textbooks:
1. Pattern Classification, Duda, Hart, Stork, 2nd edition, 2001
2. Machine Learning, Tom Mitchell, McGraw Hill 1997

Week 1: Introduction to Machine Learning and Data Mining
- Definition of machine learning and data mining
- Applications and examples
- Overview of the course
- Reading: MitchellChapter 1; Duda, Hart, Stork Chapter 1

Week 2: Probability and Bayesian Decision Theory
- Probability basics
- Conditional probability and Bayes' theorem
- Bayesian decision theory
- Reading: Duda, Hart, Stork Chapter 2; Mitchell Chapter 2

Week 3: Supervised Learning: Parametric Methods
- Linear discriminant functions and decision boundaries
- Estimation of parameters and maximum likelihood estimation
- Gaussian discriminant analysis
- Reading: Duda, Hart, Stork Chapter 3; Mitchell Chapter 3

Week 4: Supervised Learning: Non-parametric Methods
- The nearest neighbor classifier
- Parzen windows
- k-nearest neighbors
- Reading: Duda, Hart, Stork Chapter 4; Mitchell Chapter 4

Week 5: Linear Models: Regression and Classification
- Linear regression
- Logistic regression
- Perceptron algorithm
- Reading: Duda, Hart, Stork Chapter 5; Mitchell Chapter 4

Week 6: Kernel Methods: Support Vector Machines and Kernel Density Estimation
- Kernel functions and feature spaces
- Support vector machines for classification and regression
- Kernel density estimation
- Reading: Duda, Hart, Stork Chapter 6; Mitchell Chapter 9

Week 7: Decision Trees
- Tree construction and pruning algorithms
- Classification and Regression Trees (CART)
- Overfitting and generalization
- Reading: Duda, Hart, Stork Chapter 8; Mitchell Chapter 3

Week 8: Ensemble Methods: Boosting and Bagging
- The concept of ensemble learning
- Boosting algorithms: AdaBoost
- Bagging and random forests
- Reading: Duda, Hart, Stork Chapter 9; Mitchell Chapter 10

Week 9: Unsupervised Learning: Clustering
- k-means clustering
- Hierarchical clustering
- Cluster evaluation
- Reading: Duda, Hart, Stork Chapter 10; Mitchell Chapter 8

Week 10: Dimensionality Reduction and Feature Selection
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Feature selection methods: Filter, Wrapper and Embedded
- Reading: Duda, Hart, Stork Chapter 11; Mitchell Chapter 7

Week 11: Neural Networks
- Introduction to artificial neural networks
- Single-layer and multilayer perceptrons
- Backpropagation
- Reading: Duda, Hart, Stork Chapter 6; Mitchell Chapter 4

Week 12: Reinforcement Learning
- Markov decision processes
- Temporal difference learning
- Q-Learning
- Reading: Duda, Hart, Stork Chapter 13; Mitchell Chapter 13

Week 13: Evaluation, Model Selection, and Introduction to Data Mining
- Evaluation metrics for classification and regression
- Model selection and cross-validation
- Data mining: concepts, algorithms, and applications
- Reading: Duda, Hart, Stork Chapter 14; Mitchell Chapter 5

Note: While the syllabus references the relevant chapters from the given textbooks, it is expected that instructors may supplement the material with additional sources, case studies, and exercises depending on the interests and needs of the class.
assistant
Course Title: Machine Learning & Data Mining
Duration: 13 weeks
Textbooks:
1. Pattern Classification, Duda, Hart, Stork, 2nd edition, 2001
2. Machine Learning, Tom Mitchell, McGraw Hill 1997

Week 1: Introduction to Machine Learning and Data Mining
- Definition of machine learning and data mining
- Applications and examples
- Overview of the course
- Reading: MitchellChapter 1; Duda, Hart, Stork Chapter 1

Week 2: Probability and Bayesian Decision Theory
- Probability basics
- Conditional probability and Bayes' theorem
- Bayesian decision theory
- Reading: Duda, Hart, Stork Chapter 2; Mitchell Chapter 2

Week 3: Supervised Learning: Parametric Methods
- Linear discriminant functions and decision boundaries
- Estimation of parameters and maximum likelihood estimation
- Gaussian discriminant analysis
- Reading: Duda, Hart, Stork Chapter 3; Mitchell Chapter 3

Week 4: Supervised Learning: Non-parametric Methods
- The nearest neighbor classifier
- Parzen windows
- k-nearest neighbors
- Reading: Duda, Hart, Stork Chapter 4; Mitchell Chapter 4

Week 5: Linear Models: Regression and Classification
- Linear regression
- Logistic regression
- Perceptron algorithm
- Reading: Duda, Hart, Stork Chapter 5; Mitchell Chapter 4

Week 6: Kernel Methods: Support Vector Machines and Kernel Density Estimation
- Kernel functions and feature spaces
- Support vector machines for classification and regression
- Kernel density estimation
- Reading: Duda, Hart, Stork Chapter 6; Mitchell Chapter 9

Week 7: Decision Trees
- Tree construction and pruning algorithms
- Classification and Regression Trees (CART)
- Overfitting and generalization
- Reading: Duda, Hart, Stork Chapter 8; Mitchell Chapter 3

Week 8: Ensemble Methods: Boosting and Bagging
- The concept of ensemble learning
- Boosting algorithms: AdaBoost
- Bagging and random forests
- Reading: Duda, Hart, Stork Chapter 9; Mitchell Chapter 10

Week 9: Unsupervised Learning: Clustering
- k-means clustering
- Hierarchical clustering
- Cluster evaluation
- Reading: Duda, Hart, Stork Chapter 10; Mitchell Chapter 8

Week 10: Dimensionality Reduction and Feature Selection
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Feature selection methods: Filter, Wrapper and Embedded
- Reading: Duda, Hart, Stork Chapter 11; Mitchell Chapter 7

Week 11: Neural Networks
- Introduction to artificial neural networks
- Single-layer and multilayer perceptrons
- Backpropagation
- Reading: Duda, Hart, Stork Chapter 6; Mitchell Chapter 4

Week 12: Reinforcement Learning
- Markov decision processes
- Temporal difference learning
- Q-Learning
- Reading: Duda, Hart, Stork Chapter 13; Mitchell Chapter 13

Week 13: Evaluation, Model Selection, and Introduction to Data Mining
- Evaluation metrics for classification and regression
- Model selection and cross-validation
- Data mining: concepts, algorithms, and applications
- Reading: Duda, Hart, Stork Chapter 14; Mitchell Chapter 5

Note: While the syllabus references the relevant chapters from the given textbooks, it is expected that instructors may supplement the material with additional sources, case studies, and exercises depending on the interests and needs of the class.
user
Course Title: Machine Learning & Data Mining
Duration: 13 weeks
Textbooks:
1. Pattern Classification, Duda, Hart, Stork, 2nd edition, 2001
2. Machine Learning, Tom Mitchell, McGraw Hill 1997

Week 1: Introduction to Machine Learning and Data Mining
- Definition of machine learning and data mining
- Applications and examples
- Overview of the course
- Reading: MitchellChapter 1; Duda, Hart, Stork Chapter 1

Week 2: Probability and Bayesian Decision Theory
- Probability basics
- Conditional probability and Bayes' theorem
- Bayesian decision theory
- Reading: Duda, Hart, Stork Chapter 2; Mitchell Chapter 2

Week 3: Supervised Learning: Parametric Methods
- Linear discriminant functions and decision boundaries
- Estimation of parameters and maximum likelihood estimation
- Gaussian discriminant analysis
- Reading: Duda, Hart, Stork Chapter 3; Mitchell Chapter 3

Week 4: Supervised Learning: Non-parametric Methods
- The nearest neighbor classifier
- Parzen windows
- k-nearest neighbors
- Reading: Duda, Hart, Stork Chapter 4; Mitchell Chapter 4

Week 5: Linear Models: Regression and Classification
- Linear regression
- Logistic regression
- Perceptron algorithm
- Reading: Duda, Hart, Stork Chapter 5; Mitchell Chapter 4

Week 6: Kernel Methods: Support Vector Machines and Kernel Density Estimation
- Kernel functions and feature spaces
- Support vector machines for classification and regression
- Kernel density estimation
- Reading: Duda, Hart, Stork Chapter 6; Mitchell Chapter 9

Week 7: Decision Trees
- Tree construction and pruning algorithms
- Classification and Regression Trees (CART)
- Overfitting and generalization
- Reading: Duda, Hart, Stork Chapter 8; Mitchell Chapter 3

Week 8: Ensemble Methods: Boosting and Bagging
- The concept of ensemble learning
- Boosting algorithms: AdaBoost
- Bagging and random forests
- Reading: Duda, Hart, Stork Chapter 9; Mitchell Chapter 10

Week 9: Unsupervised Learning: Clustering
- k-means clustering
- Hierarchical clustering
- Cluster evaluation
- Reading: Duda, Hart, Stork Chapter 10; Mitchell Chapter 8

Week 10: Dimensionality Reduction and Feature Selection
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Feature selection methods: Filter, Wrapper and Embedded
- Reading: Duda, Hart, Stork Chapter 11; Mitchell Chapter 7

Week 11: Neural Networks
- Introduction to artificial neural networks
- Single-layer and multilayer perceptrons
- Backpropagation
- Reading: Duda, Hart, Stork Chapter 6; Mitchell Chapter 4

Week 12: Reinforcement Learning
- Markov decision processes
- Temporal difference learning
- Q-Learning
- Reading: Duda, Hart, Stork Chapter 13; Mitchell Chapter 13

Week 13: Evaluation, Model Selection, and Introduction to Data Mining
- Evaluation metrics for classification and regression
- Model selection and cross-validation
- Data mining: concepts, algorithms, and applications
- Reading: Duda, Hart, Stork Chapter 14; Mitchell Chapter 5

Note: While the syllabus references the relevant chapters from the given textbooks, it is expected that instructors may supplement the material with additional sources, case studies, and exercises depending on the interests and needs of the class.
assistant
Here's a sample syllabus for your academic course "Machine Learning & Data Mining:

Week 1: Introduction to Machine Learning – Definition, Goals, People under the computation.
Suggested Reading: Categorisation of Machine Learning – Supervised, Unsupervised, and Reinforcement.
Week 2: Decision Trees – Implementing decision trees for classification and prediction.
Suggested Reading: Decision Trees in Machine Learning – C5.0

Week 3: Reinforcement Learning – Introduction to reinforcement learning – factors important to collaborate together.
Suggested Reading: Theory of Machines and Computations by Noam Nisan, Amir Ronen, & Shimon Scully.

Week 4: Clustering and Association Rule Mining – Finding groups of data using linkages hypothesis enumeration and hill climbing.
Suggested Readings: The Theory of Association – Drew McDonald.

Week 5: Neural Networks and Deep Learning – Introduction to neural networks and deep learning algorithms methods to train neural networks automatically.
Suggested Reading: Deep Learning with Neural Networks – Lai, Matsui & Ozaki.

Week 6: Evaluation of Machine Learning – Cross-Validation, Model Selection, and Model Evaluation.
Suggested Reading: Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, Jaime G. Carbonell and S. Sathiya Keerthi.

Week 7: Bayesian Computing – Introduction Bayesian filters, learning, and density estimation.
Suggested Reading: Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon.

Week 8: Ensemble Learning – Introduction to bagging, boosting, and random forests algorithms.
Suggested Reading: Ensemble Methods: Foundations and Algorithms by David Barber.

Week 9: Support Vector Machines – Finding the largest margin boundary that separates data points in a multidimensional space.
Suggested Reading: mathworks.com/help/stats/svmnormfence.html

Week 10: Natural Language Processing – Working with text data, to represent it as a sequence of words and handling overlapping events.
Suggested Reading: Natural Language Processing with Python – Oktay Turetken.

Week 11: Emotion AI – Analysis of data patterns to recognize emotions and take action based on emotions.
Suggested Reading: Affective Computing – by Picard.

Week 12: Fairness and Biased AI – Algorithmic Bias and Fairness and measures to make AI fair.
Suggested Reading: Fairness in Machine Learning.

Week 13: Recommender Systems – Introduction to recommender systems, Collaborative Filtering algorithm and concept drift.
Suggested Reading: Collaborative Filtering – Rocío García-Retamero, Marta Plaza.

References:
assistant
Here's a sample syllabus for your academic course "Machine Learning & Data Mining:

Week 1: Introduction to Machine Learning – Definition, Goals, People under the computation.
Suggested Reading: Categorisation of Machine Learning – Supervised, Unsupervised, and Reinforcement.
Week 2: Decision Trees – Implementing decision trees for classification and prediction.
Suggested Reading: Decision Trees in Machine Learning – C5.0

Week 3: Reinforcement Learning – Introduction to reinforcement learning – factors important to collaborate together.
Suggested Reading: Theory of Machines and Computations by Noam Nisan, Amir Ronen, & Shimon Scully.

Week 4: Clustering and Association Rule Mining – Finding groups of data using linkages hypothesis enumeration and hill climbing.
Suggested Readings: The Theory of Association – Drew McDonald.

Week 5: Neural Networks and Deep Learning – Introduction to neural networks and deep learning algorithms methods to train neural networks automatically.
Suggested Reading: Deep Learning with Neural Networks – Lai, Matsui & Ozaki.

Week 6: Evaluation of Machine Learning – Cross-Validation, Model Selection, and Model Evaluation.
Suggested Reading: Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, Jaime G. Carbonell and S. Sathiya Keerthi.

Week 7: Bayesian Computing – Introduction Bayesian filters, learning, and density estimation.
Suggested Reading: Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon.

Week 8: Ensemble Learning – Introduction to bagging, boosting, and random forests algorithms.
Suggested Reading: Ensemble Methods: Foundations and Algorithms by David Barber.

Week 9: Support Vector Machines – Finding the largest margin boundary that separates data points in a multidimensional space.
Suggested Reading: mathworks.com/help/stats/svmnormfence.html

Week 10: Natural Language Processing – Working with text data, to represent it as a sequence of words and handling overlapping events.
Suggested Reading: Natural Language Processing with Python – Oktay Turetken.

Week 11: Emotion AI – Analysis of data patterns to recognize emotions and take action based on emotions.
Suggested Reading: Affective Computing – by Picard.

Week 12: Fairness and Biased AI – Algorithmic Bias and Fairness and measures to make AI fair.
Suggested Reading: Fairness in Machine Learning.

Week 13: Recommender Systems – Introduction to recommender systems, Collaborative Filtering algorithm and concept drift.
Suggested Reading: Collaborative Filtering – Rocío García-Retamero, Marta Plaza.

References:
user
Here's a sample syllabus for your academic course "Machine Learning & Data Mining:

Week 1: Introduction to Machine Learning – Definition, Goals, People under the computation.
Suggested Reading: Categorisation of Machine Learning – Supervised, Unsupervised, and Reinforcement.
Week 2: Decision Trees – Implementing decision trees for classification and prediction.
Suggested Reading: Decision Trees in Machine Learning – C5.0

Week 3: Reinforcement Learning – Introduction to reinforcement learning – factors important to collaborate together.
Suggested Reading: Theory of Machines and Computations by Noam Nisan, Amir Ronen, & Shimon Scully.

Week 4: Clustering and Association Rule Mining – Finding groups of data using linkages hypothesis enumeration and hill climbing.
Suggested Readings: The Theory of Association – Drew McDonald.

Week 5: Neural Networks and Deep Learning – Introduction to neural networks and deep learning algorithms methods to train neural networks automatically.
Suggested Reading: Deep Learning with Neural Networks – Lai, Matsui & Ozaki.

Week 6: Evaluation of Machine Learning – Cross-Validation, Model Selection, and Model Evaluation.
Suggested Reading: Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, Jaime G. Carbonell and S. Sathiya Keerthi.

Week 7: Bayesian Computing – Introduction Bayesian filters, learning, and density estimation.
Suggested Reading: Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon.

Week 8: Ensemble Learning – Introduction to bagging, boosting, and random forests algorithms.
Suggested Reading: Ensemble Methods: Foundations and Algorithms by David Barber.

Week 9: Support Vector Machines – Finding the largest margin boundary that separates data points in a multidimensional space.
Suggested Reading: mathworks.com/help/stats/svmnormfence.html

Week 10: Natural Language Processing – Working with text data, to represent it as a sequence of words and handling overlapping events.
Suggested Reading: Natural Language Processing with Python – Oktay Turetken.

Week 11: Emotion AI – Analysis of data patterns to recognize emotions and take action based on emotions.
Suggested Reading: Affective Computing – by Picard.

Week 12: Fairness and Biased AI – Algorithmic Bias and Fairness and measures to make AI fair.
Suggested Reading: Fairness in Machine Learning.

Week 13: Recommender Systems – Introduction to recommender systems, Collaborative Filtering algorithm and concept drift.
Suggested Reading: Collaborative Filtering – Rocío García-Retamero, Marta Plaza.

References:
assistant