Advanced ML Topics & Ethical Considerations (Weeks 13)
Advanced machine learning techniques focus on handling complex data types, primarily sequential data commonly found in text, speech, time series, and videos. The chapter explores Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), addressing their applications in natural language processing, time series forecasting, and association rule mining through the Apriori algorithm. It also examines recommender systems and compares content-based and collaborative filtering approaches.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- RNNs are essential for processing sequential data due to their memory capabilities, which allow them to retain information over time.
- LSTMs and GRUs address issues like vanishing gradients, making them effective for complex tasks in machine learning.
- Association Rule Mining helps uncover patterns in transactional datasets, with the Apriori algorithm providing a systematic way to identify strong rules.
Key Concepts
- -- Recurrent Neural Networks (RNNs)
- A type of neural network designed to recognize patterns in sequences of data, utilizing memory from previous inputs.
- -- Long ShortTerm Memory (LSTM)
- A variant of RNN that includes mechanisms to prevent the vanishing gradient problem, allowing it to remember information for longer sequences.
- -- Gated Recurrent Units (GRU)
- A simplified version of LSTM that combines the functions of the forget and input gates into a single update gate.
- -- Apriori Algorithm
- An algorithm used in association rule mining to find frequent itemsets by leveraging the Apriori principle that states all subsets of a frequent itemset must also be frequent.
- -- Support
- A metric indicating how often an itemset appears in the dataset, calculated as the ratio of transactions containing the itemset to the total number of transactions.
- -- Confidence
- A measure of how often items in the consequent of a rule appear in transactions that contain the antecedent, indicating the rule's reliability.
- -- Lift
- A metric that determines the strength of an association rule by comparing the likelihood of the consequent occurring with and without the antecedent.
- -- Contentbased Filtering
- A recommender system technique that suggests items based on the characteristics of items a user has previously liked or interacted with.
- -- Collaborative Filtering
- A method of making recommendations based on user-item interactions, leveraging similarities between users or items based on past behavior.
Additional Learning Materials
Supplementary resources to enhance your learning experience.