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Advanced AI: Techniques, Applications, and Ethics

via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Part of

Foundations of AI Privacy and Blockchain

Overview

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Most AI practitioners can run a model. Fewer can select the right one for the problem at hand, trace the causal story behind their data, and design systems that genuinely empower the people they affect. This course closes that gap, delivering the technical depth and ethical judgment that separate thoughtful AI expertise from surface-level familiarity. You'll classify machine learning types and apply XGBoost and CNNs to regression and classification tasks, running working Python code throughout. You'll build causal models using Bayesian networks and the DoWhy framework, integrate knowledge graphs for structured reasoning, and generate language and analyze sentiment with transformer models including GPT-2 and BERT. Then you'll program competitive AI agents using minimax algorithms and cooperative swarms with particle optimization before applying a rigorous ethics arc covering bias mitigation, privacy trade-offs, impossibility theorems, Value-Sensitive Design, and the Capability Approach. By the end of this course, you'll be able to select, build, and ethically evaluate AI systems across a range of real-world domains, equipped with both the technical skills and the principled design frameworks to ensure your work genuinely enhances human capability.

Syllabus

Distinguishing Core AI and Machine Learning Approaches

The language you use to talk about AI shapes every design decision that follows. In this module, you'll distinguish among the major types of machine learning and augmented intelligence approaches so you can recognize which method fits a given problem and begin making deliberate, informed choices about the systems you design.

Selecting and Applying ML Algorithms for Prediction and Classification

Choosing the wrong algorithm doesn’t just give you a weak model; it wastes the effort of everyone who collected the data and trusted the result. In this module, you'll apply XGBoost to regression tasks and Convolutional Neural Networks to image classification challenges, building the decision instincts needed to match an algorithmic approach to a problem's actual structure.

Analyzing Causal Relationships and Applying Knowledge-Driven Data Strategies

Most machine learning pipelines tell you what is happening in your data; far fewer tell you why. In this module, you'll construct and query causal models using Bayesian networks and the DoWhy framework, encode common-sense knowledge into AI systems using knowledge graphs, and apply pre-trained BERT models through transfer learning so your systems can reason beyond surface-level correlations even when labeled data is limited.

Building Conversational AI Applications

The next generation of applications does not wait for users to click. It listens, responds, and adapts. In this module, you'll generate coherent text using a pre-trained GPT-2 model and build a sentiment-driven appointment booking function using the Hugging Face pipeline, gaining hands-on experience with the transformer architecture and the dialogue management logic that powers conversational AI.

Building Competitive and Cooperative AI Systems

The most interesting AI problems are not solved alone. In this module, you'll apply the minimax algorithm to build a chess-playing agent that anticipates its opponent's moves, and implement particle swarm optimization to coordinate a team of drones that find targets by sharing position information across the swarm.

Detecting and Mitigating Ethical Risks in AI

AI systems do not become biased by accident. They become biased because humans teach them, and because no single design decision can satisfy every ethical standard simultaneously. In this module, you'll apply bias mitigation strategies to real datasets, weigh the trade-off that emerges when bias reduction and privacy protection conflict, and use impossibility theorems to identify why ethical conflicts in AI are structural rather than solvable by good intentions alone.

Designing Ethical and Capability-Sensitive AI Systems

Designing ethical AI is not about choosing the right framework. It is about designing systems that do not impose a framework on people who were never asked. In this module, you'll distinguish ethically paternalistic apps from empowering ones, apply the Value Sensitive Design methodology to integrate stakeholder values into the design process, and build capability-sensitive metrics using the Multidimensional Poverty Index so your AI systems optimize for human flourishing rather than simplified proxies.

Consolidating AI Expertise for Responsible Innovation

You have covered the full breadth of this course: classifying machine learning types and training XGBoost and CNN models, working through causal reasoning, knowledge graphs, and transfer learning, and engaging competitive and cooperative game theory alongside a rigorous ethics arc spanning bias, privacy, impossibility theorems, and the Capability Approach. In this module, you'll synthesize those ideas, consolidate your understanding, and commit to one concrete step in applying what you have built.