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cs189_output.txt
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Study Guide for CS 189
1. Understand the basics of machine learning, including supervised and unsupervised learning, linear regression, logistic regression, decision trees, and neural networks.
2. Be familiar with the different types of data, such as categorical, numerical, and text data.
3. Understand the different types of evaluation metrics, such as accuracy, precision, recall, and F1 score.
4. Be familiar with the different types of optimization algorithms, such as gradient descent, stochastic gradient descent, and Adam.
5. Understand the different types of regularization techniques, such as L1 and L2 regularization.
6. Be familiar with the different types of feature engineering techniques, such as one-hot encoding, feature scaling, and feature selection.
7. Understand the different types of ensemble methods, such as bagging and boosting.
8. Be familiar with the different types of deep learning architectures, such as convolutional neural networks and recurrent neural networks.
9. Understand the different types of natural language processing techniques, such as word embeddings and sentiment analysis.
10. Be familiar with the different types of reinforcement learning algorithms, such as Q-learning and SARSA.
11. Understand the different types of evaluation techniques, such as cross-validation and holdout sets.
12. Be familiar with the different types of data visualization techniques, such as scatter plots and heatmaps.
13. Understand the different types of data preprocessing techniques, such as imputation and normalization.
14. Be familiar with the different types of model selection techniques, such as grid search and random search.
15. Understand the different types of model evaluation techniques, such as confusion matrices and ROC curves.
16. Be familiar with the different types of model deployment techniques, such as Docker and Kubernetes.
17. Understand the different types of streaming algorithms, such as k-means clustering and low-dimensional decompositions.
18. Be familiar with the different types of decision theory, such as Bayesian decision theory and maximum likelihood estimation.
19. Understand the different types of locally weighted logistic regression, such as ridge regression and lasso regression.
20. Be familiar with the different types of evaluation metrics, such as precision, recall, and F1 score.
21. Understand the different types of data augmentation techniques, such as image augmentation and text augmentation.
22. Be familiar with the different types of data cleaning techniques, such as outlier detection and missing value imputation.
23. Understand the different types of data transformation techniques, such as principal component analysis and singular value decomposition.
24. Be familiar with the different types of data exploration techniques, such as correlation analysis and feature importance.
25. Understand the different types of data mining techniques, such as association rules and clustering.
26. Be familiar with the different types of data visualization libraries, such as Matplotlib and Seaborn.
27. Understand the different types of data analysis techniques, such as time series analysis and survival analysis.
28. Be familiar with the different types of data wrangling techniques, such as data aggregation and data reshaping.
29. Understand the different types of data storage techniques, such as relational databases and NoSQL databases.
30. Be familiar with the different types of data security techniques, such as encryption and access control.
31. Understand the different types of data privacy techniques, such as anonymization and pseudonymization.
32. Be familiar with the different types of data governance techniques, such as data quality management and data lineage.
33. Understand the different types of data ethics, such as privacy by design and responsible AI.
34. Be familiar with the different types of data science tools, such as Python and R.
35. Understand the different types of data science libraries, such as Scikit-learn and TensorFlow.
36. Be familiar with the different types of data science frameworks, such as PyTorch and Keras.
37. Understand the different types of data science techniques, such as supervised learning and unsupervised learning.
38. Be familiar with the different types of data science applications, such as computer vision and natural language processing.
39. Understand the different types of data science best practices, such as data exploration and model evaluation.
40. Be familiar with the different types of data science challenges, such as bias and interpretability.
41. Understand the different types of data science ethics, such as fairness and transparency.
42. Be familiar with the different types of data science tools, such as Jupyter Notebooks and Google Colab.
43. Understand the different types of data science libraries, such as Pandas and NumPy.
44. Be familiar with the different types of data science frameworks, such as Scikit-learn and TensorFlow.
45. Understand the different types of data science techniques, such as feature engineering and hyperparameter tuning.
46. Be familiar with the different types of data science applications, such as recommendation systems and anomaly detection.
47. Understand the different types of data science best practices, such as data preprocessing and model deployment.
48. Be familiar with the different types of data science challenges, such as data sparsity and data leakage.
49. Understand the different types of data science ethics, such as privacy and accountability.
50. Be familiar with the course prerequisites, such as vector calculus, linear algebra, probability, and programming experience.
51. Understand the course grading, such as homeworks, midterm, final, and project.
52. Be familiar with the course cheating policy, such as discussion of HW problems and plagiarism.
53. Understand the course core material, such as finding patterns in data, models and statistics, and optimization algorithms.
54. Be familiar with the course final exam, such as the duration, format, and topics.
Good luck studying!
> Source (Doc id: 1021e982-bc7b-4679-9466-d537e2909ec3): page_label: 2
file_name: 01.pdf
2 Jonathan Richard Shewchuk
Prerequisites
Vector calculus: Math ...
> Source (Doc id: 0f3f1ddc-786a-4ccd-9da6-e8cbbe3c7c91): page_label: 1
file_name: 189finals15.pdf
CS 189
Spring 2015Introduction to
Machine Learning Fina...